BEST AVAILABLE RETROFIT TECHNOLOGY DETERMINATION

DOMTAR INDUSTRIES INC.  ASHDOWN MILL (AFIN 41-00002) 

Prepared by: 

TRINITY CONSULTANTS

10809 Executive Center Drive 

Suite 120, The Searcy Building 

Little Rock, Arkansas 72211 

(501) 225-6400 

Jeremy W. Jewell  Senior Consultant 

Amanda S. Barber  Consultant 

TRINITY CONSULTANTS

25055 West Valley Parkway 

Suite 101 

Olathe, Kansas 66061 

(913) 390-9700 

Kasi Dubbs  Senior Consultant 

TRINITY CONSULTANTS

12770 Merit Drive 

Suite 900 

Dallas, Texas 75251 

(972) 661-8100 

Weiping Dai, PhD  Managing Consultant 

October 31, 2006 

Revised on March 26, 2007 

Project 060401.0081 

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TABLE OF CONTENTS

1. INTRODUCTION................................................................................................................ 1-1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART GUIDELINES........................... 1-1 

1.1.1 BART APPLICABILITY ........................................................................................1-1

1.1.2 BART DETERMINATION .....................................................................................1-3

2. BART-ELIGIBLE EMISSION UNITS................................................................................. 2-1

2.1 NO. 1 POWER BOILER ........................................................................................... 2-1 

2.2 NO. 2 POWER BOILER ........................................................................................... 2-2 

3. BART APPLICABILITY ANALYSIS .................................................................................. 3-1

3.1 MODELED ASHDOWN MILL EMISSIONS ................................................................ 3-1 

3.2 MODELED ASHDOWN MILL STACK PARAMETERS................................................. 3-1 

3.3 POTENTIALLY AFFECTED CLASS I AREAS............................................................. 3-2 

3.4 BART APPLICABILITY ANALYSIS RESULTS.......................................................... 3-3 

4. BART DETERMINATION ANALYSIS................................................................................ 4-1

4.1 BART DETERMINATION FOR PM.......................................................................... 4-1 

4.2 BART DETERMINATION FOR SO2 – NO. 1 POWER BOILER ................................... 4-3 

4.3 BART DETERMINATION FOR SO2 – NO. 2 POWER BOILER ................................... 4-3 

4.4 BART DETERMINATION ANALYSIS FOR NOX....................................................... 4-4 

4.4.1 STEP 1 - IDENTIFY ALL AVAILABLE RETROFIT CONTROL TECHNOLOGIES........4-4

4.4.2 STEP 2 – ELIMINATE TECHNICALLY INFEASIBLE OPTIONS...............................4-13

4.4.3 STEP 3 – EVALUATE CONTROL EFFECTIVENESS OF REMAINING CONTROL 

TECHNOLOGIES ..............................................................................................................4-18

4.4.4 STEP 4 – EVALUATE IMPACTS AND DOCUMENT RESULTS................................4-19

4.5 STEP 5 – EVALUATE VISIBILITY IMPACTS ........................................................... 4-22 

APPENDIX A – ADEQ’S DRAFT BART MODELING PROTOCOL ........................................ A-1

APPENDIX B – CONTROLS COST ANALYSES.........................................................................B-1

APPENDIX C – COMPLIANCE PLAN AND SCHEDULE ........................................................... C-1

APPENDIX D – REQUESTED DESIGN DETAILS OF NO. 1 POWER BOILER ........................... D-1

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LIST OF TABLES

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS............................................................2-1

TABLE 3-1. SUMMARY OF 24-HOUR AVERAGE MAXIMUM ACTUAL EMISSION RATES......................3-1

TABLE 3-2. STACK PARAMETERS .......................................................................................................3-2

TABLE 3-3. MODELED CLASS I AREAS ...............................................................................................3-3

TABLE 3-4. SUMMARY OF BART APPLICABILITY ANALYSIS RESULTS .............................................3-3

TABLE 4-1. BART / BOILER MACT-BASED PM EMISSION RATES....................................................4-2

TABLE 4-2. RANKING OF CONTROL STRATEGIES .............................................................................4-19

TABLE 4-3. CONTROLS COSTS SUMMARY........................................................................................4-20

TABLE 4-4. SUMMARY OF PROPOSED BART DETERMINATIONS......................................................4-21

TABLE 4-5. SUMMARY OF 24-HOUR AVERAGE MAXIMUM POST-CONTROL EMISSION RATES........4-22

TABLE 4-6. POST-CONTROL STACK PARAMETERS...........................................................................4-22

TABLE 4-7. SUMMARY OF VISIBILITY IMPROVEMENT ANALYSIS RESULTS.....................................4-23

TABLE 4-8. EMISSION UNIT & POLLUTANT SPECIFIC MODELING RESULTS ....................................4-23

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LIST OF FIGURES

FIGURE 3-1. LOCATION OF ASHDOWN MILL RELATIVE TO MODELED CLASS I AREAS .....................3-2

FIGURE 4-1. PRIMARY SNCR REACTION SEQUENCES......................................................................4-10

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1. INTRODUCTION

Domtar Industries Inc. (Domtar) owns and operates a kraft paper mill located at 285 Highway 71 

South in Ashdown, Arkansas (the Ashdown Mill). The Ashdown Mill is a major source as defined in 

Arkansas Pollution Control and Ecology Commission (ADP&E) Regulation 26, Regulations of the 

Arkansas Operating Air Permit Program, and currently operates under the authority of Arkansas 

Department of Environmental Quality (ADEQ) Operating Air Permit 0287-AOP-R6, which was 

issued on July 12, 2006.

The ADEQ has determined that the Ashdown Mill operates two emission units – No. 1 and No. 2 

Power Boilers – that are eligible to be regulated under the Best Available Retrofit Technology 

(BART) provisions of the U.S. Environmental Protection Agency’s (EPA) Regional Haze Rule in 

Title 40 of the Code of Federal Regulations (40 CFR) Part 51. BART is the primary mechanism 

identified for regulating haze-forming pollutants from stationary sources for the first implementation 

period under the Regional Haze Rule. The ADEQ has also determined, based on air dispersion 

modeling, that emissions from the Ashdown Mill BART-eligible source contributes to visibility 

impairment at a federally protected Class I area. Therefore, Domtar has prepared this report to 

document its BART determination in accordance with Appendix Y to Part 51 – Guidelines for BART 

Determinations Under the Regional Haze Rule (the BART Guidelines). 

An overview of the Regional Haze Rule and BART Guidelines is provided in Section 1.1. 

Descriptions of the Ashdown Mill’s BART-eligible emission units are included in Section 2. Section 

3 describes the BART applicability analysis completed by the ADEQ for the Ashdown Mill BARTeligible source. Domtar’s BART determination analysis is included in Section 4. 

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART GUIDELINES

The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one 

or more of 26 specific industrial source categories evaluate BART if the source was in existence 

before August 7, 1977 and began operation after August 7, 1962. “Major sources of visibilityaffecting pollutants” are sources that have the potential to emit 250 tons per year (tpy) or more of any 

of the following: oxides of nitrogen (NOX), sulfur dioxide (SO2), or particulate matter (PM).1

 The 

“BART-eligible source” is the collection of sources at a facility meeting the applicability criteria. 

1.1.1 BART APPLICABILITY

In the BART applicability analysis, a BART-eligible source is determined to be subject to 

BART if it causes or contributes to visibility impairment at one or more of the 156 

federally protected Class I areas. Per the U.S. EPA’s BART Modeling Guidance, “an 

individual source will be considered to ‘cause visibility impairment’ if the emissions 

 1

 As allowed in the BART Guidelines, the ADEQ has determined that volatile organic compounds (VOC) and 

ammonia are not visibility-affecting pollutants for the purposes of BART analyses. 

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results in a change (delta Δ) in deciviews (dv)2

 that is greater than or equal to 1.0 deciview 

on the visibility in a Class I area...if the emissions from a source results in a change in 

visibility that is greater than or equal to 0.5 dv in a Class I area the source will be 

considered to ‘contribute to visibility impairment.’” To determine whether a BARTeligible facility causes or contributes to visibility impairment, the U.S. EPA guidance 

requires the use of an air quality model, specifically recommending the CALPUFF 

modeling system, to quantify the impacts attributable to a single BART-eligible source. 

Because contribution to visibility impairment is sufficient cause to require a BART 

determination, 0.5 dv is the critical threshold for assessment of BART applicability. 

Regional haze is quantified using the light extinction coefficient (bext), which is expressed 

in terms of the haze index (HI) expressed in dv. The HI is calculated as shown in the 

following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

ln10 bext HI

The impact of a BART-eligible source is determined by comparing the HI attributable to a 

source to estimated natural background conditions. That is, a single-source visibility 

impact is measured as the change in light extinction versus background, and is referred to 

as Δdv. The background extinction coefficient is affected by various chemical species and 

the Rayleigh scattering phenomenon and can be calculated as shown in the following 

equation. 

( ) bext background SO NO OC Soil Coarse ++++++= bbbbbbb RayEC −

4 3

1 , Mm

where: 

[ ] ( ) ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

( )

[ ] 3

1

34

4 2 4

Concentrat μginion m

Humidity Relative Function

Rayleigh defaultby Mm 10 Scattering

EC10

Mass Coarse6.0

Soil1

OC4

NONH3

SONH3

3

4

=

=

=

=

=

=

=

=

=

−

RHf

b

b

b

b

b

b RHf

b RHf

Ray

EC

Coarse

Soil

OC

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh air todue scattering is Scattering molecules

concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes Mass Coarse dusts coarse ofion 

concentrat thedenotes Soil soils fine ofion 

concentrat thedenotes OC carbon organic ofion 

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

Values for the parameters listed above specific to the natural background conditions at 

each Class I area are provided on an annual-average basis in the U.S. EPA’s Guidance for 

Estimating Natural Visibility Conditions under the Regional Haze Rule.

3

 2

 The deciview (dv) is a metric used to represent normalized light extinction attributable to visibility-affecting 

pollutants. 3

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 

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Particulate species that affect visibility are emitted from anthropogenic (human-caused) 

sources and include coarse particulate matter (PMC), fine particulate matter (PMF), and 

elemental carbon (EC) as well as precursors to secondary organic aerosols (SOA) and fine 

particulate matter such as SO2 and NOX. The extinction coefficient due to emissions of 

visibility-affecting pollutants from a single BART-eligible source is calculated according 

to the following equation. 

( ) b sourceext SO NO SOA PMF PMC +++++= bbbbbb EC

−

4 3

1

, Mm

where: 

[( ) ] ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

[ ] 3

34 3

4 2 4 4

Concentrat μginion m

Humidity Relative Function

EC10

PMC6.0

PMF1

SOA4

NONH3

SONH3

=

=

=

=

=

=

=

=

RHf

b

b

b

b

b RHf

b RHf

EC

PMC

PMF

SOA

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ] concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes PMC PM coarse ofion 

concentrat thedenotes PMF PM fine ofion 

concentrat thedenotes SOA secondary ofion aerosols organic

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

1.1.1.1 CALPUFF MODELING ANALYSES

As stated above, the BART Guidelines recommend using the CALPUFF 

modeling system to compute the 24-hour average visibility impairment 

attributable to a BART-eligible source to assess whether the 0.5 Δdv 

contribution threshold is exceeded, and if so, the frequency, duration, and 

magnitude of any exceedance events. CALPUFF is a refined air quality 

modeling system that is capable of simulating the dispersion, chemical 

transformation, and long-range transport of multiple visibility-affecting 

pollutant emissions and is therefore preferred for BART applicability and 

determination analyses. 

1.1.2 BART DETERMINATION

BART-eligible sources that are found to cause or contribute to visibility impairment at a 

Class I area are required to make a BART determination. The BART Guidelines define 

BART as follows: 

BART means an emission limitation based on the degree of reduction 

achievable through the application of the best system of continuous emission 

reduction for each pollutant which is emitted by...[a BART-eligible source]. 

The emission limitation must be established, on a case-by-case basis, taking 

into consideration the technology available, the costs of compliance, the 

energy and non-air quality environmental impacts of compliance, any pollution 

control equipment in use or in existence at the source, the remaining useful life 

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of the source, and the degree of improvement in visibility which may 

reasonably be anticipated to result from the use of such technology. The BART 

analysis identifies the best system of continuous emission reduction taking into 

account: 

(1) The available retrofit control options, 

(2) Any pollution control equipment in use at the source (which affects 

the availability of options and their impacts), 

(3) The costs of compliance with control options, 

(4) The remaining useful life of the facility, 

(5) The energy and non-air quality environmental impacts of control 

options[, and]

(6) The visibility impacts analysis. 

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2. BART-ELIGIBLE EMISSION UNITS

The BART Guidelines define the following three steps for determining which emission units at a 

facility are BART-eligible: 

1. Identify the emission units in the BART source categories, 

2. Identify the start-up dates of those units, and 

3. Compare potential emissions to the 250 ton/yr cutoff. 

“Fossil-fuel boilers of more than 250 million BTUs per hour heat input” are one of the listed BART 

source categories. The Ashdown Mill’s No. 1 and No. 2 Power Boilers are each greater than 250 

million British thermal units per hour (MMBtu/hr), were in existence on August 7, 1977, began 

operation after August 7, 1962, and each have potential emissions greater than 250 tpy of PM, NOX, 

or SO2; therefore, these units make up the Ashdown Mill’s BART-eligible source. A summary of the 

BART eligibility criteria for each emission unit is provided in Table 2-1. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS

 

Emission Unit 

Source 

Number 

BART 

Source 

Category 

Year of 

Completion of 

Construction or 

Reconstruction 

Potential 

SO2

Emissions

(tpy) 

Potential 

NOX

Emissions 

(tpy) 

Potential

PM/PM10

Emissions

(tpy) 

No. 1 Power Boiler SN-03 Boiler a 1968 214.0 1,084.1 1,502.3 

No. 2 Power Boiler SN-05 Boiler a 1976 4,305.5 2,514.1 359.2 a

 Fossil-fuel boilers of more than 250 million BTUs per hour heat input. 

Detailed descriptions of each unit are provided in the sub-sections below. 

2.1 NO. 1 POWER BOILER

The No. 1 Power Boiler (SN-03), also known as the Bark Boiler, was installed in 1968. It has a heat 

input rating of 580 MMBtu/hr and an average steam generation rate of approximately 120,000 pounds 

per hour (lb/hr). It combusts primarily bark (approximately 75 percent of the heat input is supplied 

by bark), but is also permitted to burn bark and wood chips used to absorb oil spills, wood waste, 

recycled sanitary products composed of cellulose and polypropylene, pelletized paper fuel (PPF), tirederived fuel (TDF), municipal yard waste, No. 6 fuel oil, reprocessed fuel oil, used oil generated on 

site, and natural gas. Natural gas is only used to supplement other fuels during high steam demand 

periods. Fuel oil usage is limited to 2,700,000 gallons per year, and the sulfur content of the fuel oil 

used is limited to 3.0 percent by weight. TDF usage (total for No.1, No. 2, and No. 3 Power Boilers) 

is limited to 220 tons per day. 

The No. 1 Power Boiler is equipped with a traveling grate, a combustion air system, and 

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multiclones. 

The No. 1 Power Boiler is not subject to any New Source Performance Standards (NSPS) in 40 CFR 

Part 60. It is subject to 40 CFR Part 63, Subpart DDDDD, National Emissions Standard for 

Hazardous Air Pollutants (NESHAP) for Industrial, Commercial, and Institutional Boilers and 

Process Heaters. NESHAP DDDDD establishes Maximum Achievable Control Technology 

(MACT) limits and is commonly referred to as “the Boiler MACT.” 

To meet the applicable Boiler MACT PM emission standard of 0.07 lb/Mmbtu, Domtar is preparing 

to install a wet electrostatic precipitator (WESP) on the No. 1 Power Boiler. 

2.2 NO. 2 POWER BOILER

The No. 2 Power Boiler (SN-05) started operations in February 1976. It has a heat input rating of 820 

MMBtu/hr and an average steam generation rate of approximately 600,000 lb/hr. It combusts 

primarily bituminous coal (over 80 percent of the heat input is supplied by coal), but is also permitted 

to burn bark, bark and wood chips used to absorb oil spills, wood waste, petroleum coke (pet coke), 

recycled sanitary products based on cellulose and polypropylene, PPF, TDF, municipal yard waste, 

No. 6 fuel oil, reprocessed fuel oil, used oil generated on site, natural gas, and non-condensable gases 

(NCGs). The NCGs are produced in the pulp area (from the cooking of chips) and evaporator area 

(where weak black liquor is concentrated) and consist of nitrogen, total reduced sulfur (TRS) 

compounds, methanol, acetone, SO2, and minor quantities of other compounds such as methyl ethyl 

ketone (MEK). Under normal operating conditions, natural gas is not combusted. 

The No. 2 Power Boiler is equipped with a traveling grate, combustion air system including overfire 

air, multiclones, and two parallel venturi scrubbers. The SO2 loading to the boiler is significant since 

the boiler burns coal and NCGs. Therefore, the scrubbing fluid includes water and a source of alkali, 

such as sodium hydroxide (i.e., caustic) and/or pulp mill extraction stage filtrate. 

The No. 2 Power Boiler is subject to 40 CFR 60, Subpart D, Standards of Performance for FossilFuel-Fired Steam Generators for Which Construction is Commenced After August 17, 1971, 40 CFR 

60, Subpart BB, Standards of Performance for Kraft Pulp and Paper Mills (since it combusts NCGs), 

and 40 CFR Part 63, Subpart DDDDD, National Emissions Standard for Hazardous Air Pollutants 

for Industrial, Commercial, and Institutional Boilers and Process Heaters. 

The No. 2 Power Boiler is equipped with Continuous Emissions Monitoring Systems (CEMS) for 

NOX, SO2, and carbon monoxide (CO). In accordance with 40 CFR 60, Subpart BB, the No. 2 Power 

Boiler also has a continuous flame pyrometer to measure the temperature at the point of NCG 

injection (the temperature at the injection point must remain at or above 1200 oF for at least 0.5 

seconds at all times that NCGs are being burned).

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3. BART APPLICABILITY ANALYSIS

This section summarizes the source-specific inputs and results of the BART applicability analysis 

conducted by the ADEQ for Domtar’s Ashdown Mill BART-eligible source. The screening modeling 

methodologies and data resources used by the ADEQ in executing the CALPUFF modeling system 

are described in technical detail in the ADEQ’s Draft BART Modeling Protocol (the Protocol), dated 

June 7, 2006, and in the Central Regional Air Planning Association (CENRAP) BART Modeling 

Guidelines (issued on December 22, 2005, and re-issued on February 3, 2006). A copy of the 

Protocol is included in Appendix A. 

3.1 MODELED ASHDOWN MILL EMISSIONS

Whereas the BART eligibility determination relies on current potential emissions of visibilityaffecting pollutants, the BART applicability modeling analysis is based on maximum 24-hour average 

actual emission rates of NOX, SO2, and PM10 for the modeled three-year period (i.e., 2001, 2002, & 

2003).4

 At the ADEQ’s request, Domtar estimated the 24-hour average maximum actual emission 

rates of visibility-affecting pollutants from the No. 1 and No. 2 Power Boilers using a combination of 

CEMS data, source-specific stack testing results, and emission factors from U.S. EPA’s AP-42. 

These emission rates are summarized in Table 3-1. 

TABLE 3-1. SUMMARY OF 24-HOUR AVERAGE MAXIMUM ACTUAL EMISSION RATES

 

Emission Unit 

NOX

Emissions

(lb/hr) 

SO2

Emissions

(lb/hr) 

PM10/PMF 

Emissions 

(lb/hr) 

No. 1 Power Boiler 179.6 442.5 169.5 

No. 2 Power Boiler 526.8 788.2 81.6 

3.2 MODELED ASHDOWN MILL STACK PARAMETERS

Actual stack parameters were input to the CALPUFF model to represent each emissions point. The 

location of each point was represented using the Lambert Conformal Coordinate (LCC) system. 

According to the Protocol, because the BART modeling focuses on mesoscale transport to Class I 

areas, effects of building downwash were not considered in the ADEQ’s analysis. Table 3-2 

summarizes the stack parameters modeled for the BART-eligible emission units at Domtar’s 

Ashdown Mill. 

 4

 The ADEQ assumed all PM10 emissions were PMF for modeling purposes. 

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TABLE 3-2. STACK PARAMETERS

 

Emission Unit 

LCC East 

(km) 

LCC North

(km) 

Base 

Elevation

(m) 

Stack 

Height 

(m) 

Stack 

Diameter 

(m) 

Exhaust 

Temperature

(K) 

Exhaust 

Velocity

(m/s) 

 

No. 1 Power Boiler 267.47491 -698.66686 97.5 66.1 1.890 522 26.76 

No. 2 Power Boiler 267.48245 -698.74355 97.5 71.6 3.659 325 11.92 

 

3.3 POTENTIALLY AFFECTED CLASS I AREAS

Regardless of distance from the BART-eligible source, the ADEQ evaluated all Class I areas within 

300 km of the Arkansas state boundary in all analyses. Figure 3-1 illustrates the location of the 

Ashdown Mill relative to each of the modeled Class I areas. 

FIGURE 3-1. LOCATION OF ASHDOWN MILL RELATIVE TO MODELED CLASS I AREAS

Base map provided by ADEQ. 

Ashdown Mill 

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Table 3-3 presents the Class I areas (and responsible Federal Land Manager [FLM]) included in 

ADEQ’s analyses and the approximate distance from each area to the Ashdown Mill. 

TABLE 3-3. MODELED CLASS I AREAS

 

Class I Area FLM a

Approximate Distance from 

Ashdown Mill (km) 

 

Caney Creek Wilderness FS 85 

Upper Buffalo Wilderness FS 250 

Hercules-Glades Wilderness FS 350 

Mingo Refuge FWS 510 

Sipsey Wilderness FS 620 

a

 FS = Forest Service (Department of Agriculture), FWS = Fish and Wildlife Service (Department of Interior). 

3.4 BART APPLICABILITY ANALYSIS RESULTS

The ADEQ’s BART applicability analysis showed that Domtar’s Ashdown Mill contributes to 

visibility impairment, since the maximum modeled 24-hour average impacts were greater than 0.5 

Δdv, in the Caney Creek, Upper Buffalo, Hercules-Glades, and Mingo Class I areas. The results of 

the ADEQ’s BART applicability analysis for Domtar’s Ashdown Mill are summarized in Table 3-4. 

TABLE 3-4. SUMMARY OF BART APPLICABILITY ANALYSIS RESULTS

 

Class I Area 

Maximum 24-hour 

Impact (Δdv) a

Number of Days 

> 0.5 Δdv a

Number of Days 

> 1.0 Δdv a

 

Caney Creek 2.262 159 50 

Upper Buffalo 1.181 18 1 

Hercules-Glades 0.701 3 0 

Mingo 0.923 2 0 

Sipsey 0.341 0 0 

 

a

 For total modeled period: years 2001, 2002, and 2003. 

Since the ADEQ’s BART applicability analysis shows that Domtar’s Ashdown Mill BART-eligible 

source contributes to visibility impairment in at least one Class I area, Domtar must conduct a BART 

determination analysis for the No. 1 and No. 2 Power Boilers.

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4. BART DETERMINATION ANALYSIS

In general, BART is determined for each eligible emissions unit using the following five (5) steps 

from Section IV.D of the BART Guidelines: 

Step 1 – Identify all available retrofit control technologies, 

 Step 2 – Eliminate technically infeasible options, 

 Step 3 – Evaluate control effectiveness of remaining control technologies, 

 Step 4 – Evaluate impacts and document the results, and 

 Step 5 – Evaluate visibility impacts. 

However, in the preamble to the BART Guidelines, the U.S. EPA clearly encourages the use of 

streamlined approaches for BART determinations so that states and industry can focus their resources 

on the main contributors to visibility impairment.5

 Domtar asserts that streamlined BART 

determinations are appropriate for emissions of PM and SO2 from the Ashdown Mill’s No. 1 and No. 

2 Power Boilers. The streamlined BART determinations for PM and SO2 are presented in Sections 

4.1, 4.2, and 4.3. Steps 1 through 4 of the BART determination analysis for NOX emissions from the 

No. 1 and No. 2 Power Boilers are presented in Section 4.3. Section 4.5 presents the visibility 

impacts evaluation for all pollutants. 

4.1 BART DETERMINATION FOR PM 

Section IV.C of the BART Guidelines describes a streamlined approach for evaluating BART for 

certain sources that are subject to MACT standards (i.e., NESHAP in 40 CFR 63). The Ashdown 

Mill’s No. 1 and No. 2 Power Boilers are affected sources (in the existing, large, solid fuel 

subcategory) under the Boiler MACT, and are subject to a PM emissions standard of 0.07 lb/MMBtu. 

Since the Boiler MACT standard was established recently the technology analysis is up-to-date. The 

No. 1 and No. 2 Power Boilers must be in compliance with the Boiler MACT standards by September 

13, 2007, in advance of the anticipated 2013 BART compliance deadline. Domtar is planning to 

equip the No. 1 Power Boiler with a WESP to meet the PM standard. The No. 2 Power Boiler is 

equipped with a wet scrubber and can meet the Boiler MACT PM emission standard. Domtar has not 

identified any feasible upgrades to the No. 2 Power Boiler’s wet scrubber. At ADEQ’s request, 

Domtar evaluated the costs for installing a WESP on the No. 2 Power Boiler. The estimated cost 

effectiveness, based on the estimates given in the proposal for the WESP on the No. 1 Power Boiler, 

is at a minimum $30,000/ton – clearly infeasible. Table 4-1 presents the maximum PM emission 

rates from each power boiler based on heat input capacity and the Boiler MACT standard. 

 5

 Federal Register, Vol. 70, No. 128, July 6, 2005, pp 39107 and 39116. 

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TABLE 4-1. BART / BOILER MACT-BASED PM EMISSION RATES

 

Emission Unit 

PM Emissions 

(lb/hr) 

PM Emissions 

(tpy) 

No. 1 (Bark) Power Boiler 40.6 177.9 

No. 2 (Coal) Power Boiler 57.4 251.5 

The recent Boiler MACT PM emission standard is presumptively relied upon to meet BART 

requirements. Accordingly, a comprehensive BART determination analysis is not necessary to 

determine BART for PM emissions from the Ashdown Mill’s No. 1 and No. 2 Power Boilers. The 

ADEQ agreed to allow this streamlined MACT-equals-BART option in a September 8, 2006, letter, 

but required that Domtar “consult with the ADEQ Air Division regarding whether the wet 

electrostatic precipitator (MACT Control) is the best available and cost effective control technology 

for PM2.5.”6

 Domtar provides the following evidence that a WESP is the best choice for control of the 

No. 1 Power Bark Boiler. 

Particulate emissions from wood-fired boilers are typically controlled by one of four technologies: 

baghouse (fabric filter), ESP (wet or dry), wet scrubber, or cyclone. Cyclones provide for the lowest 

control efficiencies of the options at up to 65 percent, and particulate collection efficiencies of 85 

percent or greater have been reported for venturi [wet] scrubbers operating on wood-fired boilers.7

 

To achieve control efficiencies of 90 percent or greater, a baghouse or ESP is used. The normal PM 

control efficiency range for a fabric filter is 95 to 99+ percent, and the normal PM control efficiency 

range for a WESP is 98 to 99+ percent.8

 Fabric filters are rarely used on wood-fired boilers due to 

concerns about bag flammability.9

 The principal drawback is a fire danger arising from the collection 

of combustible carbonaceous fly ash.10 Both types (i.e., wet and dry) of ESPs are capable of greater 

than 99 percent removal of particle sizes above 1 micron.11 An additional benefit of WESPs is that 

the wash used in WESPs can also have some control effect on other pollutant gases via absorption 

and can help condense other emissions due to the cooling of the stream.12 Based on the comparison 

of control efficiencies and the applicability of each control device, Domtar asserts that the WESP is 

the best control technology (i.e., BART) for the No. 1 Power Boiler. 

 6

 Mike Bates (ADEQ), letter to Kelley Crouch (Domtar), September 8, 2006. 7

 U.S. EPA, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Source (AP42), Fifth Edition, Section 1.6 – Wood Residue Combustion in Boilers, September 2003. 8

 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 9

 NCASI, Information on Retrofit Control Measures for Kraft Pulp Mill Sources and Boilers for NOX, SO2 and 

PM Emissions, Corporate Correspondence Memo 06-014. 10 U.S. EPA, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Source (AP42), Fifth Edition, Section 1.6 – Wood Residue Combustion in Boilers, September 2003. 11 Northeast States for Coordinated Air Use Management (NESCAUM) and Mid-Atlantic/Northeast Visibility 

Union (MANE-VU), Assessment of Control Technology Options for BART-Eligible Sources – Steam Electric Boilers, 

Industrial Boilers, Cement Plant and Paper and Pulp Facilities, March 2005. 12 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 

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4.2 BART DETERMINATION FOR SO2 – NO. 1 POWER BOILER

Generally, pre-combustion SO2 control strategies involve fuel switching/blending or fuel cleaning so 

that less fuel-bound sulfur enters the process. However, because wood already contains very little 

sulfur, pre-combustion SO2 controls are ineffective. 

Post-combustion SO2 control is accomplished by reacting the SO2 in the gas with a reagent (usually 

calcium-based [e.g., lime or limestone] or sodium-based [e.g., caustic]) and removing the resulting 

product (a sulfate/sulfite) for disposal or commercial use. SO2 reduction technologies are commonly 

referred to as flue gas desulfurization (FGD) and/or scrubbers and are usually described in terms of 

the process conditions (wet versus dry), byproduct utilization (throwaway versus saleable) and 

reagent utilization (once-through versus regenerable).13 Post-combustion SO2 controls have not been 

installed on wood-fired boilers because of the relatively low SO2 emissions from wood-combustion 

(due to the low sulfur content of wood). 

Due to the low fuel sulfur input, emissions from wood combustion are inherently low and have a 

negligible impact on visibility impairment. Therefore, Domtar proposes no additional add-on control, 

i.e., only the existing fuel restrictions (fuel oil sulfur content and usage limitations) and no additional 

SO2 removal as BART for SO2 emissions from the No. 1 Power Boiler. 

4.3 BART DETERMINATION FOR SO2 – NO. 2 POWER BOILER

Section IV.D.1.9 of the BART Guidelines provides an option to skip the comprehensive BART 

determination analysis for BART-eligible emission units that are already equipped with the most 

stringent controls available (including any possible improvements to the control device) “as long 

these most stringent controls available are made federally enforceable for the purpose of 

implementing BART for that source.” The Ashdown Mill’s No. 2 Power Boiler is equipped with a 

wet scrubber for control of SO2 (and particulate) emissions. The existing wet scrubber achieves an 

SO2 control efficiency of approximately 90 percent, which is within the normal range for the highest 

efficiency SO2 control strategies and is the BART-based control efficiency presumed by the Central 

Regional Air Planning Association (CENRAP) and the Midwest Regional Planning Organization 

(MRPO) for pulp and paper industry power boilers.14,15

The No. 2 Power Boiler is equipped with a CEMS for SO2. Thus, Domtar is able to immediately 

identify needs for both ongoing operational adjustments and periodic maintenance and/or scrubber 

improvements to maintain high levels of SO2 control. Domtar has not identified any feasible 

upgrades to the existing wet scrubber. It should be noted that the No. 2 Power Boiler is operated such 

that SO2 emissions are well below any applicable limits/standards. Since wet scrubbing is the most 

effective method of controlling SO2 emissions, no additional analysis is needed for SO2 emissions 

from the No. 2 Power Boiler. Domtar proposes no additional SO2 removal as BART for the No. 2 

Power Boiler. 

 13 NESCAUM and MANE-VU, Assessment of Control Technology Options for BART-Eligible Sources – Steam 

Electric Boilers, Industrial Boilers, Cement Plant and Paper and Pulp Facilities, March 2005. 14 CENRAP’s Control Estimates Spreadsheet dated January 10, 2006. 15 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures, March 29, 2005. 

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4.4 BART DETERMINATION ANALYSIS FOR NOX

Each required step of the BART determination analysis for emissions of NOX from the No. 1 and No. 

2 Power Boilers is presented below. 

4.4.1 STEP 1 - IDENTIFY ALL AVAILABLE RETROFIT CONTROL TECHNOLOGIES

The BART Guidelines require the consideration of all “control technologies with a 

practical potential for application to the emissions unit and the regulated pollutant under 

evaluation.” The list of available control options should include “the most stringent option 

and a reasonable set of options for analysis...[, but] it is not necessary to list all 

permutations of available control levels that exist for a given technology – the list is 

complete if it includes the maximum level of control each technology is capable of 

achieving.” 

Per the BART Guidelines, the BART determination analysis must “take into account 

technology transfer of controls that have been applied to similar source categories and gas 

streams [in addition to] existing controls for the source category in question.” However, 

“technologies which have not yet been applied to (or permitted for) full scale operations 

need not be considered as available; [the U.S. EPA does] not expect the source owner to 

purchase or construct a process or control device that has not already been demonstrated in 

practice.” The BART Guidelines provides the following additional considerations for 

preparing the list of potential control options: 

3⁄4 One of the control options should reflect the level of control equivalent to 

any applicable NSPS, 

3⁄4 Source redesign should not be considered, 

3⁄4 Fuel switching should not be considered, and 

3⁄4 For emission units with existing control measures or devices, one of the 

control options should involve improvements to the existing controls. 

Potential NOX control technologies and resulting emission control quantities for the 

Ashdown Mill’s No. 1 and No. 2 Power Boilers were identified from the exhaustive review 

of the U.S. EPA’s Clean Air Technology Center, including the RACT/BACT/LAER 

Clearinghouse (RBLC), control equipment vendor information, publicly-available air 

permits and applications, and technical literature published by the U.S. EPA, the Regional 

Planning Organizations (RPOs), and industry groups such as the National Council for Air 

and Stream Improvement, Inc. (NCASI).16 In fact, Domtar has largely relied upon the 

extensive research conducted by NCASI regarding the applicability and effectiveness of 

each control option for coal- and wood-fired pulp and paper mill power boilers. Each NOX

 16 NCASI is an independent, non-profit research institute that focuses on environmental topics of interest to the 

forest products industry. NCASI was established in 1943...In the years since, NCASI has developed technical expertise 

spanning the spectrum of environmental challenges facing the forest products industry, and is today recognized as the 

leading source of reliable data on environmental issues affecting this industry. (http://www.ncasi.org/about/default.aspx) 

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control option identified as potentially applicable to either power boiler is listed below and 

explained in detail in the following subsections. 

3⁄4 Selective Non-Catalytic Reduction (SNCR) / NOXOUT 

3⁄4 Selective Catalytic Reduction (SCR) 

3⁄4 Low NOX Burners (LNB) and Ultra Low NOX Burners (ULNB) 

3⁄4 Over-fire Air (OFA) 

3⁄4 Reburning / Methane de-NOX (MdN) 

3⁄4 Flue Gas Recirculation (FGR) (Internal and External) 

3⁄4 Fuel Blending / Boiler Operational Modifications / Tuning / Optimization 

For this analysis, utility boiler control technology determinations were generally not

considered since utility boilers and pulp and paper mill power boilers are considered too 

dissimilar. 

The greatest difference in utility and power boiler operations is the fluctuating 

steam demand characteristic of pulp and paper mill operations which requires 

that power boilers continuously adjust fuel firing rates and excess air levels. 

Even with the most sophisticated combustion controls, it is not practical or safe 

to maintain excess air continuously at minimum levels. Consequently, power 

boilers have characteristically and inherently higher NOX emissions. 

...NOX reduction measures are particularly difficult to implement in small, low 

capacity facilities because a) residence time is limited and often inadequate for 

applying OFA without excessive loss of thermal efficiency or induced smoking; 

b) relatively small furnace dimensions limit combustion modifications that 

increase flame length and tend to cause the flame to impinge on tube wall;, c) 

peak boiler efficiency and minimized NOX emissions occur close to minimum 

flue-gas O2 content, which is at the threshold of smoke or combustibleemissions formation; d) steam is used far more effectively in industrial 

applications than in conventional electric utility plants and, consequently, 

emission limits based on boiler heat input or volume of flue gas do not 

recognize such efficiency.17

Combustion-related NOX emissions are formed by two mechanisms. NOX formed from 

oxidation of molecular nitrogen (N2) in combustion air is referred to as “thermal NOX” and 

is dependent on high temperatures (approximately 2,800 °F) and an excess of combustion 

air. NOX formed by oxidation of nitrogen compounds in fuel is referred to as “fuel NOX.” 

The NOX formed from coal combustion is primarily fuel NOX.

18 Fuel NOX is also the 

dominant NOX formation mechanism operative during wood combustion because wood 

combustion in boilers seldom reaches high enough temperatures.19,20 

 17 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

18 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 19 NCASI, Information on Retrofit Control Measures for Kraft Pulp Mill Sources and Boilers for NOX, SO2 and 

PM Emissions, Corporate Correspondence Memo 06-014. 

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The possible NOX emissions control technologies generally fit into one of two categories: 

combustion modifications, which are often associated with improving boiler performance, 

or flue gas treatment (i.e., post-combustion controls). Pre-combustion techniques to reduce 

fuel NOX have shown little promise.21 Combustion modifications are the most common, 

commercially available means of controlling NOX emissions from fossil fuel-fired 

boilers.22 However, since wood-fired boilers normally burn at lower temperatures (around 

1,500 °F), the units have inherently lower NOX emissions, and, as a result, NOX

combustion control technologies are not applicable to wood-fired boilers.

23 During the 

past decade, LNB with FGR and LNB alone were the most commonly recommended NOX

control technologies for oil/gas and coal-fired boilers, respectively, while good combustion 

control was typically the only recommendation for wood waste-fired boilers.24

COMBUSTION MODIFICATIONS

4.4.1.1 FLUE GAS RECIRCULATION

Generally, FGR involves extracting a portion (15 to 30 percent) of the flue gas 

and readmitting it to the furnace through the burner window. When the flue 

gas is extracted from the economizer or air heater outlet, a separate fan/blower 

is needed to withdraw the flue gas. This setup is referred to as external or 

forced FGR. Internal or induced FGR refers to the setup where the flue gas is 

extracted from upstream of the stack using the forced draft (FD) fan instead of 

a separate FGR fan. In either setup, the recirculated flue gas acts as a thermal 

diluent (i.e., heat sink) to reduce combustion temperatures. It also dilutes the 

combustion reactants and reduces the excess air requirements thereby reducing 

the concentration of oxygen in the combustion zone. Thus, thermal NOX

formation is inhibited.25 The onset of thermal NOX occurs around 2,800 °F, 

and NOX generation increases exponentially with temperatures beyond 2,800 

°F. As only thermal NOX can be controlled by this technique, it is especially 

effective only in oil and gas-fired units.26

4.4.1.2 LOW NOX BURNERS / ULTRA LOW NOX BURNERS

LNB technology utilizes advanced burner design to reduce NOX formation 

through the restriction of oxygen, flame temperature, and/or residence time. A 

 20 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

21 Ibid. 

22 Ibid. 

23 STAPPA and ALAPCO, Controlling Fine Particulate Matter Under the Clean Air Act: A Menu of Options, 

March 2006. 24 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

25 U.S. EPA, Clean Air Technology Center, Nitrogen Oxides (NOX), Why and How They Are Controlled. 

Research Triangle Park, North Carolina, EPA-456/F-99-006R, November 1999. 26 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

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LNB is a staged combustion process that is designed to split fuel combustion 

into two zones, primary combustion and secondary combustion. Two general 

types of LNB exist: staged fuel and staged air. Lower emission rates can be 

achieved with a staged fuel burner than with a staged air burner. Staged fuel 

LNB separate the combustion zone into two regions. The first region is a lean 

primary combustion region where the total quantity of combustion air is 

supplied with a fraction of the fuel. Combustion in the primary region (first 

stage) takes place in the presence of a large excess of oxygen at substantially 

lower temperatures than a standard burner. In the second region, the remaining 

fuel is injected and combusted with any oxygen left over from the primary 

region. The remaining fuel is introduced in the second stage outside of the 

primary combustion zone so that the fuel/oxygen are mixed diffusively (rather 

than turbulently), which maximizes the reducing conditions. This technique 

inhibits the formation of thermal NOX, but has little effect on fuel NOX. By 

increasing residence times staged air LNB provide reducing conditions, which 

have a greater impact on fuel NOX than staged fuel burners. The estimated 

NOX control efficiency for LNB in high temperature applications is 25 

percent.27

The application of LNB is often limited by the longer flames produced as a 

consequence of improved air distribution control. While there is generally 

ample room for LNB flames in utility furnaces, their use on smaller power 

boilers can result in flame impingement on furnace walls, leading to tube wall 

overheating and mechanical failure. Flame impingement can also result in 

premature flame quenching and increased soot and CO emissions.28

ULNB combine LNB and FGR technologies and may incorporate other 

techniques such steam injection. The FGR design within ULNB recirculates 

flue gas from the flame or firebox back into the combustion zone in an effort to 

reduce oxygen concentrations without significantly reducing flame 

temperature. Reduced oxygen concentrations in the flame have a strong impact 

on fuel NOX.

29 ULNB also tend to have large diameters, but shorter flame 

lengths and may be easier to retrofit.30

Combustion modification with LNB is used in both gas/oil-fired and coal-fired 

units.31 LNB are not used for wood-fired boilers. The No. 1 Power Boiler 

burns only a small amount of fuel for which LNB technology exists. 

Therefore, LNB is not considered further for the No. 1 Power Boiler. 

 27 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 28 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

29 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 30 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

31 Ibid. 

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4.4.1.3 OVERFIRE AIR

In OFA, about 10 to 20 percent of the combustion air flow is directed to 

separate air ports located downstream of the burners. OFA works by reducing 

the excess air in the burner zone, thereby enhancing the combustion staging 

effect and theoretically reducing NOX emissions. Residual unburned material, 

such as CO and unburned carbon, which inevitably escapes the main burner 

zone, is oxidized as the OFA is admixed later.32

OFA vendors (e.g., Jansen Combustion and Boiler Technologies, Inc.) have 

informed Domtar that while OFA often results in decreased NOX emissions, the 

primary purpose is combustion optimization, and implementation of OFA can 

actually increase NOX emissions in certain circumstances. Domtar has 

experienced this potential adverse effect. A recent OFA upgrade to the 

Ashdown Mill’s No. 3 Power Boiler (not a BART-eligible unit) is still in 

startup mode, but so far Domtar has measured a noteworthy increase in NOX

emissions. 

Domtar does not consider OFA to be a potential NOX control technology, and 

OFA is not considered further in this analysis. 

4.4.1.4 REBURNING / METHANE DE-NOX

In reburning, also known as “off-stoichiometric combustion” or “fuel staging,” 

a fraction (5 to 25 percent) of the total fuel heat input is diverted to a second 

combustion zone downstream of the primary zone. The fuel in the fuel-rich 

secondary zone acts as a reducing agent, reducing NO, which is formed in the 

primary zone, to N2. Low nitrogen-containing fuels such as natural gas and 

distillate oil are typically used for reburning to minimize further NOX

formation. Generally, it is more economical for a facility to use the same fuel 

for reburning as it does for primary combustion, although there are exceptions. 

In order to use coal as a reburning fuel, it must be finely ground, which 

requires additional pulverizing equipment.33

MdN utilizes the injection of natural gas together with recirculated flue gases 

(for enhanced mixing) to create an oxygen-rich zone above the combustion 

grate. Air is then injected at a higher furnace elevation to burn out the 

combustibles. This process is claimed to yield between 50 and 70 percent NOX

reduction and to be suitable for all solid fuel-fired stoker boilers. However, as 

of 2002, MdN had only been demonstrated for a short duration in one pulp mill 

wood-fired stoker boiler that also burned small amounts of waste treatment 

plant residuals, with NOX reductions of 40 to 50 percent reported.34

 32 Ibid. 

33 STAPPA and ALAPCO, Controlling Fine Particulate Matter Under the Clean Air Act: A Menu of Options, 

March 2006. 34 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

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More recently, MdN is being applied to kraft pulp mill stoker boilers by 

utilizing the VOC content of NCGs to partially replace the natural gas (by up to 

25 percent). This technology has been tested for over a year at one pulp mill 

boiler, and is being tested at several boilers within one forest products industry 

(FPI) company.35 

4.4.1.5 FUEL BLENDING

Since wood is inherently low in nitrogen content, fuel blending is not feasible 

for wood-fired boilers. Therefore, this control strategy is not considered for the 

Ashdown Mill’s No. 1 Power Boiler. 

Coal-fired boilers could experience a decrease in NOX emissions from fuel 

blending. Preliminary results show that the co-firing of up to 7 percent 

biomass, on a heat-input basis, with crushed or pulverized coal can lower NOX

emissions by as much as 15 percent.36 However, fuel biasing on an industrial 

boiler subject to rapid and excessive load swings could result in too rich or too 

lean firing conditions, which can lead to flame stability problems and explosive 

conditions.37 In addition, unlike utilities, which can specify the nitrogen 

content of their large oil purchases, most industrial mills cannot.38

Domtar historically mixes 10 to 15 percent (heat input basis) wood with coal in 

the No. 2 Power Boiler. Therefore, fuel blending is considered part of the base 

case for the No. 2 Power Boiler. 

4.4.1.6 BOILER OPERATIONAL MODIFICATIONS / TUNING / OPTIMIZATION

Combustion optimization efforts can lead to improvements in NOX emissions 

of 5 to 15 percent. Recent developments of intelligent controls – softwarebased systems that "learn" to operate a unit and then maintain its performance 

during normal operation – are expected help in keeping plants well tuned.39 

Domtar has employed, and will continue to employ, the latest boiler 

optimization and tuning techniques. This control strategy is considered part of 

the base case for the Ashdown Mill’s No. 1 and No. 2 Power Boilers. 

 35 Ibid. 

36 Ibid. 

37 Ibid. 

38 Ibid. 

39 NESCAUM and MANE-VU, Assessment of Control Technology Options for BART-Eligible Sources – Steam 

Electric Boilers, Industrial Boilers, Cement Plant and Paper and Pulp Facilities, March 2005. 

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POST-COMBUSTION CONTROLS

4.4.1.7 SELECTIVE NON-CATALYTIC REDUCTION

SNCR is a post-combustion NOX control technology based on the reaction of 

urea or ammonia (NH3) and NOX. In the SNCR chemical reaction, urea or 

ammonia-based chemicals are injected into the combustion gas path to reduce 

the NOX to nitrogen and water. The primary SNCR reaction sequences are 

shown in Figure 4-1.40

FIGURE 4-1. PRIMARY SNCR REACTION SEQUENCES 

Typical NOX removal efficiency for SNCR is 30 to 65 percent. For industrial 

coal-fired boilers, SNCR can achieve approximately 40 percent NOX control.41 

An important consideration for implementing SNCR is the operating 

temperature range. The optimum temperature range is approximately 1,600 to 

2,000 °F.42 Operation at temperatures below this range results in ammonia slip. 

Operation above this range results in oxidation of ammonia, forming additional 

NOX. In addition, the urea must have sufficient residence time, about 3 to 5 

seconds, at the optimum operating temperatures for efficient NOX reduction. 

Therefore, the injection point is typically prior to convective heat recovery.43 

 40 ABB Power Plant Laboratories, Engineering development of coal-fired high performance power systems – 

Phase II topical report, Selective Non-Catalytic Reduction System Development Subcontract to United Technologies 

Research Center, Contract No. DE-AC22-95PC95144, February 24, 1997 (reprinted in NCASI’s Special Report No. 03-04). 41 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures, March 29, 2005. 42 U.S. EPA, Clean Air Technology Center, Nitrogen Oxides (NOX), Why and How They Are Controlled. 

Research Triangle Park, North Carolina, EPA-456/F-99-006R, November 1999. 43 U.S. EPA. Summary of NOX Control Technologies and their Availability and Extent of Application. Research 

Triangle Park, North Carolina. EPA-450/3-92-004, February 1992. 

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According to the U.S. EPA, the performance of an SNCR system is affected by 

six factors. 

These are a) inlet NOX level, b) temperature, c) mixing, d) 

residence time, e) reagent-to- NOX ratio, and f) fuel sulfur content. 

Lower inlet NOX concentrations reduce the reaction kinetics and 

hence the achievable NOX emissions reductions. As mentioned 

above, temperatures below the desired window result in ammonia 

emissions (slip), and temperatures above the desired window result 

in NH3 being oxidized to NOX. Mixing becomes an important 

consideration in regions distant from an injection nozzle where the 

level of turbulence is reduced and stratification of the reagent and 

flue gas will probably be a greater problem, especially at low 

boiler loads. Residence time becomes important to allow the 

desired reactions to go to completion. Small, packaged, water 

tube boilers and boilers with varying steam loads are therefore 

difficult applications for SNCR. As higher than the theoretical NH3

to NOX ratios are generally required to achieve desired NOX

emission reductions, a trade-off exists between NOX control and 

the presence of NH3 in the flue gas. The main disadvantage of 

SNCR is the low NOX reduction that is experienced when the 

allowable ammonia slip is low. Finally, in the case of high sulfur 

fuels, excess NH3 can react with sulfur trioxide to form ammonium 

sulfate salt compounds that deposit on downstream equipment 

leading to plugging and reduced heat transfer efficiencies.44

One concern about the SNCR process is its ability to perform adequately under 

changing load and fuel conditions.45 Based on its research regarding this 

concern, NCASI concludes that SNCR is most widely used for base-loaded 

boilers, and is not suited for power boilers that experience wide temperature 

variances, i.e., high load swings. NCASI also points out that the use of SNCR 

systems on coal-fired boilers is still in the development stage.46

The NOXOUT process is an SNCR hybrid based on the following chemical 

reaction that ideally occurs in the temperature range of 1700 to 2000 °F: 

2 NO + NH2CONH2 + 1/2 O2 → 2 N2 + CO2 + 2 H2O 

 44 U.S. EPA, New source performance standards, subpart Db – technical support for proposed revisions to NOX, 

EPA-453-/R-95-012 (republished in NCASI’s Special Report 03-04). 45 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

46 Ibid. 

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The problems with typical SNCR systems (e.g., ammonia slippage and heat 

transfer surface fouling with byproduct formation) also exist with the NOXOUT 

process. 

4.4.1.8 SELECTIVE CATALYTIC REDUCTION

SCR is a post-combustion gas treatment process in which NH3 is injected into 

the exhaust gas in the presence of a catalyst bed usually located between the 

boiler and air preheater. The catalyst lowers the activation energy required for 

NOX decomposition.47 On the catalyst surface, NH3 and nitric oxide (NO) react 

to form diatomic nitrogen and water. The overall chemical reaction can be 

expressed as: 

4NO + 4NH3 + O2 → 4N2 + 6H2O 

When operated within the optimum temperature range of approximately 575 to 

750 °F, the reaction can result in removal efficiencies between 70 and 90 

percent. For coal-fired industrial boilers, SCR can achieve approximately 80 

percent NOX control.48 The specific temperature ranges are 600 to 750 °F for 

conventional (vanadium or titanium) catalysts, 470 to 510 °F for platinum 

catalysts, and 600 to 1000 °F for high-temperature zeolite catalysts.49 SCR 

units have the ability to function effectively under fluctuating temperature 

conditions (usually ± 50 °F), although fluctuation in exhaust gas temperature 

reduces removal efficiency by disturbing the chemical kinetics (speed) of the 

NOX -removal reaction. 

According to the U.S. EPA, the performance of an SCR system is affected by 

six factors. 

These are a) NOX level at SCR inlet, b) flue gas temperature, c) 

NH3-to-NOx ratio, d) fuel sulfur content, e) gas flow rate, and f) 

catalyst condition. For SCR, when inlet NOX concentrations fall 

below 150 ppm, the reduction efficiencies decrease with 

decreasing NOX concentrations. Each type of catalyst has an 

optimum operating temperature range. Temperatures below this 

range result in ammonia emissions (slip), and temperatures above 

the desired range result in NH3 being oxidized to NOX. For up to 

about 80 percent NOX reduction efficiencies, a 1:1 NH3:NOX ratio 

is sufficient. For higher efficiencies, higher reagent to NOX ratios 

are required which may result in higher NH3 slip. In the case of 

high sulfur fuels, excess NH3 can react with sulfur trioxide to form 

ammonium sulfate salt compounds that deposit and foul 

downstream equipment. SCR application experience in the case of 

 47 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 48 MRPO, Interim White Paper – Midwest RPO Candidate Control Measures, March 29, 2005. 49 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 

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medium-to-high sulfur fuels is limited. For a given flue gas flow 

rate, the catalyst structural design should be chosen so that the 

residence time needed for the reduction reactions to take place on 

the catalyst surface is achievable.50

4.4.2 STEP 2 – ELIMINATE TECHNICALLY INFEASIBLE OPTIONS

Per the BART Guidelines, documentation of infeasibility should “explain, based on 

physical, chemical, or engineering principles, why technical difficulties would preclude the 

successful use of the control option under review.” The BART Guidelines use the two key 

concepts of “availability” and “applicability” to determine if a control option is technically 

feasible. These concepts are defined in Section IV.D.2: 

...a technology is considered "available" if the source owner may obtain it 

through commercial channels, or it is otherwise available within the common 

sense meaning of the term. An available technology is "applicable" if it can 

reasonably be installed and operated on the source type under consideration. 

The typical stages for bringing a control technology concept to reality as a 

commercial product are: 

• concept stage; 

• research and patenting; 

• bench scale or laboratory testing; 

• pilot scale testing; 

• licensing and commercial demonstration; and 

• commercial sales. 

A control technique is considered available, within the context presented 

above, if it has reached the stage of licensing and commercial availability. 

Similarly, we do not expect a source owner to conduct extended trials to learn 

how to apply a technology on a totally new and dissimilar source type. 

Consequently, you would not consider technologies in the pilot scale testing 

stages of development as “available” for purposes of BART review. 

In general, a commercially available control option will be presumed 

applicable if it has been used on the same or a similar source type. Absent a 

showing of this type, you evaluate technical feasibility by examining the 

physical and chemical characteristics of the pollutant-bearing gas stream, and 

comparing them to the gas stream characteristics of the source types to which 

the technology had been applied previously. 

COMBUSTION MODIFICATIONS

 50 U.S. EPA, New source performance standards, subpart Db – technical support for proposed revisions to NOX, 

EPA-453-/R-95-012 (republished in NCASI’s Special Report 03-04). 

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4.4.2.1 FLUE GAS RECIRCULATION

FGR is used to reduce thermal NOX formation. Emissions due to fuel-bound 

NOX, which are significant for coal-fired boilers, are not meaningfully affected 

by FGR. Therefore, FGR is not technically feasible to control NOX emissions 

from coal-fired boilers.51 Similarly, FGR would not be effective in wood 

combustion since most of the NOX generated during wood combustion is also 

from the fuel NOX pathway.52 Recent refusals by vendors (e.g., Entropy 

Technology & Environmental Consultants LP53) to provide budgetary estimates 

for installing FGR are further evidence that FGR is not applicable for the 

Ashdown Mill’s No. 1 and No. 2 Power Boilers. 

4.4.2.2 REBURNING / METHANE DE-NOX

Generally, Domtar considers MdN not feasible because (1) it is not fully 

demonstrated and (2) it incorporates FGR, which is clearly technically 

infeasible (see Section 4.4.2.1). However, Domtar was able to obtain 

equipment cost estimates from vendors of MdN. Therefore, MdN is considered 

further in this analysis. 

POST-COMBUSTION MODIFICATIONS

NCASI points out the following issues of concern for post-combustion NOX

controls (i.e., SNCR and SCR) for pulp and paper mill power boilers:54

Load Swings - Pulp mill combination and power boilers 

frequently exhibit wide and rapid load swings that are not 

consistent with the steady conditions required for effective use of 

either SNCR or SCR NOX control technologies. The load swings 

produce variable temperature conditions in the boiler, causing the 

temperature zone for NOX reduction to fluctuate, making it more 

difficult to know where to inject the reactants. 

Temperature Incompatibility - Combination and power boilers 

are affected by temperature profile incompatibility. To obtain the 

required temperature window, the only location to install this 

technology is upstream of the particulate matter control device, yet 

this is where flue gases are dirty and can foul the catalyst rapidly. 

 51 U.S. EPA. Alternative Control Technologies Document: NOX Emissions from Utility Boilers. (EPA-453/R-94-

023). 52 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

53 Steve Wood (ETEC), e-mail to Joel Martin (Domtar), September 20, 2006: “Based on the design and 

operational data provided regarding #2 Coal Boiler, ETEC would decline to bid the application Induced Flue Gas 

Recirculation for Boiler #2 NOX control. Flue gas recirculation technology is very effective in reducing natural gas and 

light oil fuel NOX emissions, but is not for No.6 fuel oil, coal, bark and other solid fuels. To the best of our knowledge, flue 

gas recirculation for NOX control has never been installed on a coal fired boiler.” 54 Ibid. 

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Downstream of the PM control device, the temperature is too low 

for the catalyst to be effective. 

Unproven – SCR or SNCR controls, technologies which, for the 

most part, are untested and infeasible for pulp and paper mill 

boilers. These technologies must be operated on a continuous 

basis within a specified temperature range in order to be effective. 

The type of fuel burned influences the design of the technology, 

and FPI facilities’ frequent fuel changes and co-firing of multiple 

fuels would result in design and operational problems. 

Lack of Guarantee for FPI Boilers – Boiler owners are finding 

that vendors of SCR and SNCR technologies are unwilling to 

provide performance guarantees that the controls will meet the 

level of reduction called for in [NSPS Subpart Db (promulgated on 

September 16, 1998)]. 

4.4.2.3 SELECTIVE NON-CATALYTIC REDUCTION

Most boilers in the pulp and paper industry operate in the swing load mode, a 

consequence of supplying steam as required to the various components of the 

process. The problem with control of the required flue gas temperature 

window is an inherent difficulty with use of SNCR for load-following boilers, 

whether wood or fossil fuel.55 

Controlling flue gas temperatures over the entire range of operating loads that 

the boiler is expected to experience will be very difficult to achieve. Boilers in 

the pulp and paper industry rarely operate under base loaded conditions. 

Consequently, the location of the desired temperature window is expected to 

change constantly. Accurate, instantaneous temperature measurement, as well 

as the ability to accurately adjust the location of the injection nozzle, would be 

necessary. Ammonia slip would be a recurring problem associated with the 

application of the SNCR process to industrial boilers with fluctuating loads.56

Inadequate reagent dispersion in the region of reagent injection in wood-fired 

boilers is also a factor mitigating against the use of SNCR technology.57 Good 

dispersion of the reagent in the flue gas is needed to get good utilization of the 

reagent and to avoid excessive ammonia slip from the process. The need for a 

 55 NCASI, Information on Retrofit Control Measures for Kraft Pulp Mill Sources and Boilers for NOX, SO2 and 

PM Emissions, Corporate Correspondence Memo 06-014. 56 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

57 NCASI, Information on Retrofit Control Measures for Kraft Pulp Mill Sources and Boilers for NOX, SO2 and 

PM Emissions, Corporate Correspondence Memo 06-014. 

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sufficient volume in the boiler at the right temperature window precludes the 

application of SNCR in all types of industrial boilers.58

Additional issues with SNCR include the potential for formation of ammonium 

sulfate salts (if sulfur oxides are present in the gas stream where they can react 

with excess ammonia from the SNCR process to form ammonium salts), which 

cause plugging problems. Ammonia also poses potential water quality issues - 

ammonia slip released to the atmosphere could contaminate surface waters by 

deposition. 

SNCR has been applied to a few base-loaded wood and combination woodfired boilers, mainly in the electric generating industry. However, its efficacy 

on wood-fired boilers with changing loads has not been demonstrated, except 

when used as a polishing step. Early use of ammonia injection in the case of 

one pulp mill wood-fired boiler met with significant problems and had to be 

abandoned (significant ammonia slip, caused by inefficient dispersion of the 

reagent within the boiler, was to blame). The boiler was unable to meet the 

manufacturer guarantee unless operated at less than half load. Even then, 

reducing NOX to near permitted limits consumed considerably more ammonia 

than anticipated, leading to the formation of a visible ammonium chloride 

plume. A similar problem was encountered at a second FPI mill where nearly 

half the urea (on a molar basis) injected was being emitted as ammonia.59 

The use of SNCR on stoker type wood-fired boilers that have significant load 

swings has not been demonstrated. Excessive ammonia slip is a primary 

concern when adequate dispersion of the SNCR chemical is not achieved in the 

boiler ductwork within the range of residence times available and temperatures 

needed for the NOX reduction reactions to go to completion. Additional 

concerns include the impact of interference from higher CO levels present in 

many wood-fired boilers, the possibility of appreciable SNCR chemical being 

absorbed onto the ash matrix in a wood-fired boiler, and the extent and fate of 

ammonia in scrubber purge streams.60

The MRPO concludes, “if combustion zone temperatures within the boiler do 

not fall into [the ideal temperature range], then SNCR would be infeasible.”61 

4.4.2.4 SELECTIVE CATALYTIC REDUCTION

The use of SCR on boilers operating in the FPI has also never been 

successfully demonstrated for wood boilers, and would face the same inherent 

problem of requiring it to be post PM-control to protect the catalyst, and 

 58 NESCAUM and MANE-VU, Assessment of Control Technology Options for BART-Eligible Sources – Steam 

Electric Boilers, Industrial Boilers, Cement Plant and Paper and Pulp Facilities, March 2005. 59 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

60 Ibid. 

61 MACTEC, Midwest RPO Boiler BART Engineering Analysis, March 30, 2005. 

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achieving and maintaining the required temperature window for effective NOX

control.62 There are numerous other issues with using SCR including catalyst 

plugging and soluble alkali poisoning as well as increased energy 

consumption.63

The use of SCR technology would be considered technically infeasible based 

upon the fact that post-particulate removal flue gas temperatures are typically 

significantly lower than those desired for this application. Many boilers are 

equipped with wet scrubbers for particulate emission (PM) control. Reheating 

the scrubbed flue gases from these boilers to bring them within the desired 

temperature window would involve a significant energy penalty. For preparticulate removal flue gas application, catalyst deactivation from high 

particulate loading would be a serious concern, in addition to the impact of 

fluctuating loads on flue gas temperatures. Deactivation and/or poisoning 

could result from the size and density of fly ash particulate, and from their 

unique chemical and physical nature. Water-soluble alkali (such as Mg or Na) 

in particulate-laden gas streams has been known to poison SCR catalysts. 

Space considerations for installing a catalyst section in an existing boiler’s 

ductwork are also important. Also note the use of solid fuels can result in 

catalyst contamination even with efficient PM control system and high 

moisture levels in exhaust air would result in inefficient SCR operation.64

Most boilers feature a flue gas temperature at the economizer exit that is below 

the ammonium sulfate/bisulfate dew point. Air heater surfaces must withstand 

corrosion from ammonium sulfates and bisulfates, be easily cleaned with 

conventional soot blowing, and survive corrosion-inducing water washing. 

SO3 produced by the catalyst may condense on cooler surfaces, depending on 

the temperature, during both steady-state and non-steady-state operation. 

Higher levels of SO2 to SO3 conversion could cause accelerated corrosion or 

higher SO3-induced plume opacity. Minimizing ammonia levels in the stack 

(typically <2 to 3 ppm) is required to avoid problems with disposal of scrubber 

byproduct contaminated by ammonia. The use of a particular catalyst puts 

restrictions on the fuel flexibility for a boiler. For example, purchasing coal 

with fly ash containing calcium oxide and arsenic outside the defined range 

absolves the catalyst supplier from responsibility for arsenic poisoning.65

The only “wood-fired” boiler SCR application in service in the U.S. was 

located at a woodworking facility in Ohio. This SCR was located downstream 

of a mechanical collector and electrostatic precipitator, operating in flue gas 

temperatures ranging from 550 to 650 °F. The only problem reported at this 

 62 NCASI, Information on Retrofit Control Measures for Kraft Pulp Mill Sources and Boilers for NOX, SO2 and 

PM Emissions, Corporate Correspondence Memo 06-014. 63 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. 

64 Ibid. 

65 Ibid. 

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installation was minor catalyst blinding due to the deposition of fine particulate 

that escaped the PM collection devices. It was learned the operating 

temperature for this SCR system allowed the use of conventional catalysts 

designed to accommodate high dust applications. For these catalysts, the 

catalyst openings through which the flue gas flows are sized to provide proper 

surface area contact and sufficient flue gas velocity to minimize fouling. Low 

temperature catalyst designs are considerably different and would not be 

recommended for use on any high dust application. Based on this description 

of the air pollution control system configuration and the operating conditions 

for this particular wood-fired boiler, it is important to identify several specific 

differences between this installation and those that operate in the FPI. First, 

due to the requirement to provide hot air to burn all but the driest of wood 

fuels, wood-fired boilers are usually equipped with air preheaters. Thus, even 

when dry particulate control devices like an ESP are utilized, the installation of 

an SCR catalyst section after a PM control device is not amenable for 

adaptation to such boilers without, of course, incurring a severe energy penalty. 

Second, a significant portion of the FPI’s wood-fired boilers is controlled for 

PM emissions by multiclones and wet scrubbers. Therefore the PM emissions 

from these would be higher than the example situation. Third, it is unclear how 

the Ohio facility’s SCR system would have worked under the fluctuating boiler 

load characteristics common to many FPI boilers. Finally, sawdust, which was 

the fuel fired in the Ohio facility’s boiler, is a low moisture fuel and the 

particulate matter present in the flue gases from its combustion is likely to be 

of different composition than when bark or hog fuel (typically much higher 

moisture) is burned.66

Hence the use of SCR technology has clearly not been demonstrated for 

industrial wood, biomass or combination fuel-fired boilers in the FPI.67

4.4.3 STEP 3 – EVALUATE CONTROL EFFECTIVENESS OF REMAINING CONTROL 

TECHNOLOGIES

Table 4-2 presents a ranking of the technically feasible control strategies in order of their 

effectiveness (i.e., potential control efficiency). For controls with a range of performance 

levels, the BART Guidelines note: 

It is not [the U.S. EPA’s] intent to require analysis of each possible level of 

efficiency for a control technique as such an analysis would result in a large 

number of options. It is important, however, that in analyzing the technology 

you take into account the most stringent emission control level that the 

technology is capable of achieving. 

 66 Ibid. 

67 Ibid. 

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TABLE 4-2. RANKING OF CONTROL STRATEGIES

 

Control Strategy Applicability 

Potential Control 

Efficiency (%) 

MdN No. 1 & No. 2 Boilers 50 a

LNB No. 2 Boiler Only 30 b,c 

Original OFA + Boiler 

Tuning/Optimization No. 1 Boiler Base Case 

Original OFA + NOX Air + Fuel 

Blending + Boiler Tuning/ 

Optimization 

No. 2 Boiler Base Case 

 

a

 Based on estimate from Energy System Associates. 

b

 NCASI, NOX Control in Forest Products Industry Boilers: A Review of Technologies, Costs and Industry 

Experience, Special Report 03-04. c

 Based on estimate from B&W. 

Note that MdN is included in Table 4-2 despite its questionable technical feasibility. 

4.4.4 STEP 4 – EVALUATE IMPACTS AND DOCUMENT RESULTS

The technically feasible control technologies are evaluated on the basis of (1) costs of 

compliance, including consideration of the remaining useful life, (2) energy impacts, and 

(3) non-air quality environmental impacts. 

For the purposes of this analysis, energy and non-air quality environmental impacts are 

considered minimal for all the technically feasible control options listed in Table 4-2. Per 

the BART Guidelines, the costs of compliance analysis for each control option consists of 

comparisons of the average cost effectiveness and the incremental cost effectiveness, 

which are defined in Section IV.D.4 as follows: 

Average cost effectiveness means the total annualized costs of control divided 

by the annual emissions reduction (the difference between baseline annual 

emissions and the estimate of emissions after controls), using the following 

formula: 

Average cost effectiveness (dollars per ton removed) = Control option 

annualized cost ÷ (Baseline annual emissions – Annual emissions with Control 

option) 

...the incremental cost effectiveness calculation compares the costs of 

performance level of a control option to those of the next most stringent option, 

as shown in the following formula (with respect to cost per emissions 

reduction): 

Incremental Cost Effectiveness (dollars per incremental ton removed) = (Total 

annualized costs of control option) – (Total annualized costs of next control 

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option) ÷ (Control option annual emissions) – (Next control option annual 

emissions) 

The average and incremental (where applicable) cost effectiveness for each feasible control 

option for the Ashdown Mill’s No. 1 and No. 2 Power Boilers are summarized in Table 

4-3. Detailed control costs calculations are presented in Appendix B. 

TABLE 4-3. CONTROLS COSTS SUMMARY

 

 Total NOX Cost Effectiveness 

Emission Unit 

Control 

Strategy 

Annualized 

Cost (MM$) 

Removed

(tpy) 

Average 

($/ton) 

Incremental 

($/ton) 

No. 1 Power Boiler MdN 3.94 542 7,262 17,354 

No. 2 Power Boiler MdN 5.35 1,257 4,259 9,571 

 LNB 1.10 754 1,465 b N/A 

b

 This estimate is consistent with NCASI’s Special Report 03-04, which states, “for pulverized coal boilers, a 30 

percent NOX reduction could be achieved with LNB at a cost of <$2,000/ton.” 

Based on Domtar’s analysis, MdN is considered cost prohibitive for both the No. 1 and No. 

2 Power Boilers and is ruled out as a BART option. Based on steps 1 through 4 of the 

BART determination analysis, no retrofit controls are available for the No. 1 Power Boiler 

and LNB is the best available retrofit control technology for the No. 2 Power Boiler. 

PROPOSED BART DETERMINATIONS FOR NOX

For the No. 1 and No. 2 Power Boilers, Domtar proposes NOX BART limits of 179.6 lb/hr 

and 368.7 lb/hr, respectively. 

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A summary of all proposed BART determinations is provided in Table 4-4. Please note 

that while example control technologies theoretically capable of achieving the proposed 

BART limits are listed, Domtar reserves the right to implement other equivalent control 

strategies between now and the BART effective date (~2013) to meet the same emission 

limits. 

TABLE 4-4. SUMMARY OF PROPOSED BART DETERMINATIONS

 

Emission Unit Pollutant BART Limit 

Example Control 

Technology 

No. 1 Power Boiler PM 0.07 lb/MMBtu (Boiler MACT) WESP 

 SO2 442.5 lb/hr No additional add-on 

controls (existing fuel 

restrictions) 

 NOX 179.6 lb/hr No add-on controls 

No. 2 Power Boiler PM 0.07 lb/MMBtu (Boiler MACT) Wet Scrubber 

 SO2 788.2 lb/hr Wet Scrubber 

 NOX 368.7 lb/hr (30 Percent Control) LNB 

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4.5 STEP 5 – EVALUATE VISIBILITY IMPACTS

The degree of visibility improvement is assessed based on the change in modeled impacts for the precontrol (i.e., the BART applicability analysis) and post-control (i.e., the predicted maximum 24-hour 

emission rate after implementation of BART) emission scenarios. Per the BART Guidelines, this 

assessment “may consider the frequency, magnitude, and duration components of [visibility] 

impairment.” 

The post-control modeling for the visibility improvement analysis was conducted using the 

CALPUFF modeling system in the same manner as the ADEQ’s BART applicability analysis, which 

is described in Section 3 of this report and in the Protocol (see Appendix A). In fact, the post-control 

modeling was conducted using the same CALPUFF, POSTUTIL, and CALPOST input files 

generated by the ADEQ for the applicability analysis. The only changes made to these files for the 

post-control modeling was to the emissions rates and stack parameter changes associated with 

implementing the chosen BART controls. Table 4-5 and Table 4-6 summarize the maximum 24-hour 

average emission rates and the stack parameters, respectively, that were modeled in the post-control 

analysis. 

TABLE 4-5. SUMMARY OF 24-HOUR AVERAGE MAXIMUM POST-CONTROL EMISSION RATES

 

Emission Unit 

NOX

Emissions

(lb/hr) 

SO2

Emissions

(lb/hr) 

Total PM

Emissions 

(lb/hr) 

No. 1 Power Boiler 179.6 442.5 40.6 

No. 2 Power Boiler 368.7 788.2 57.4 

TABLE 4-6. POST-CONTROL STACK PARAMETERS

 

Emission Unit 

LCC East 

(km) 

LCC North

(km) 

Elevation

(m) 

Stack

Height

(m) 

Stack 

Diameter 

(m) 

Exhaust 

Temperature

(K) 

Exhaust 

Velocity

(m/s) 

 

No. 1 Power Boiler 267.47491 -698.66686 97.5 66.1 1.890 522 26.76 

No. 2 Power Boiler 267.48245 -698.74355 97.5 71.6 3.659 325 11.92 

 

Visibility improvement is quantified and judged in a cumulative matter. That is, to compare to the 

pre-control modeling analysis executed by the ADEQ, Domtar’s post-control modeling analysis 

simulated all emissions reductions from both emission units. Note that since maximum applicability 

analysis impacts were less than 0.5 Δdv for the Sipsey Class I area, this area was not evaluated in the 

post-control scenario. Table 4-7 summarizes the results of the visibility improvement analysis. 

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TABLE 4-7. SUMMARY OF VISIBILITY IMPROVEMENT ANALYSIS RESULTS

 

Class I Area 

Maximum 24-hour 

Impact (Δdv) a

Number of Days 

> 0.5 Δdv a

Number of Days 

> 1.0 Δdv a

 

Caney Creek 2.039 118 29 

Upper Buffalo 1.029 14 1 

Mingo 0.836 2 0 

Hercules-Glades 0.631 2 0 

 

a

 For total modeled period: years 2001, 2002, and 2003. 

As shown in Table 4-7, the application of BART on the Ashdown Mill’s No. 1 and No. 2 Power 

Boilers results in significant visibility impacts improvement in the affected Class I areas. Visibility 

impairment at Upper Buffalo was reduced by 29 percent while impairment at Caney Creek was 

reduced by 32 percent (based on total impact and excluding any days with impacts less than 0.50 

Δdv). The number of days within the modeled three-year period with impacts greater than 0.50 Δdv 

decreased from 159 to 118 for the Caney Creek Class I area and from 18 to 14 for the Upper Buffalo 

Class I area. 

In addition to the cumulative analysis, the ADEQ requested emission unit specific and pollutant 

specific modeling. Since cumulative analysis impacts in the Upper Buffalo and Mingo Class I areas 

are minimal, the emission unit and pollutant specific modeling was only conducted for the Caney 

Creek Class I area. The results of these pre- and post-control analyses (each conducted for the entire 

modeling period: year 2001, 2002, and 2003) are presented in Table 4-8. 

TABLE 4-8. EMISSION UNIT & POLLUTANT SPECIFIC MODELING RESULTS

 

 Pre-Control Scenario Post-Control Scenario 

Emission 

Unit Pollutant 

Max. 24-hour 

Impact (Δdv) 

Number of 

Days > 0.5 Δdv 

Max. 24-hour 

Impact (Δdv) 

Number of 

Days > 0.5 Δdv 

PM 0.252 0 0.065 0 

SO2 0.575 2 0.575 2 

No. 1 

Power 

Boiler NOX 0.398 0 0.398 0 

PM 0.135 0 0.095 0 

SO2 1.036 5 1.036 5 

No. 2 

Power 

Boiler NOX 1.072 35 0.762 14 

PM 0.391 0 0.156 0 

SO2 1.542 30 1.542 30 

No. 1 & 2 

Power 

Boilers NOX 1.427 54 1.129 36 

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Additionally, as requested by the ADEQ in its September 8, 2006, letter, Domtar’s post-control (and 

pre-control, where different from the ADEQ’s applicability modeling files) CALPUFF, POSTUTIL, 

and CALPOST input files and CALPOST output files are included with this report on electronic 

media. The file naming convention is explained below. Note that all filenames contain the “doas” 

root (characters 4 through 7) to denote Domtar – Ashdown. Note also that path names will need to be 

modified to represent the user’s directory structure when replicating these analyses. 

File Naming Convention: 

CALPUFF & POSTUTIL x x _doas yy(v*) . fff 

xx = Model: cp = CALPUFF 

 pu = POSTUTIL 

 

yy = Year: 01 = 2001 

 02 = 2002 

 03 = 2003 

 

v* = Pollutant Run Identifier A = PM Pre-controls 

 B = PM Post-controls 

 C = SO2 Pre- and Post-controls 

 D = NOX Pre-controls 

 E = NOX Post-controls 

 

fff = File type: inp = Input 

 

CALPOST x x _doas yyz(v*) . fff 

xx = Model: ct = CALPOST 

 

yy = Year: 01 = 2001 

 02 = 2002 

 03 = 2003 

 

z = Class I area: c = Caney Creek 

 m = Mingo 

 u = Upper Buffalo 

 h = Hercules-Glades 

 s = Sipsey 

 

v* = Pollutant Run Identifier A = PM Pre-controls 

 B = PM Post-controls 

 C = SO2 Pre- and Post-controls 

 D = NOX Pre-controls 

 E = NOX Post-controls 

 

fff = File type: inp = Input 

 lst = Output 

The “v” designator is used only for the unit and source specific model runs requested by ADEQ. 

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APPENDIX A – ADEQ’S DRAFT BART MODELING PROTOCOL

ASHDOWN-Q8-000618
STATE OF ARKANSAS 

DEPARTMENT OF ENVIRONMENTAL QUALITY 

8001 NATIONAL DRIVE 

P.O. BOX 8913 

LITTLE ROCK, ARKANSAS 72219-8913 

PHONE: (501) 682-0744 

FAX: (501) 682-0753

Draft 

BEST AVAILABLE RETROFIT 

TECHNOLOGY (BART) 

MODELING PROTOCOL 

June 7, 2006 

Prepared by: 

Mary Pettyjohn, Senior Epidemiologist 

Planning and Air Quality Analysis Branch 

Air Division 

 

ASHDOWN-Q8-000619
ii 

Table of Contents 

 List of Figures..................................................................................................... iii 

 List of Tables ...................................................................................................... iv 

 I. Introduction...........................................................................................................1 

 II. Background...........................................................................................................2 

 III. BART-Eligible Sources ........................................................................................4 

 IV. CAIR and Arkansas ..............................................................................................8 

 V. BART Air Quality Modeling Approach ...............................................................8 

 VI. BART-Eligible Units Physical Parameters.........................................................10 

 A. Stack parameters ............................................................................................10 

 B. Emission rates ................................................................................................10 

 VII. Air Quality Model and Inputs.............................................................................11 

 A. CALPUFF No-obs and refined screening modeling......................................12 

 1. Modeling domain......................................................................................12 

 2. CALPUFF system implementation...........................................................13 

 3. Meteorological data modeling (CALMET) ..............................................13 

 4. Dispersion modeling (CALPUFF)............................................................14 

 5. Post-processing (POSTUTIL/CALPOST)................................................16 

 6. Measuring visibility impacts.....................................................................17 

VIII. Change in Visibility Due to BART Controls......................................................19 

 IX. References...........................................................................................................20 

Appendix A. Proposed Time-line.............................................................................. A-1 

Appendix B. Map of Receptors..................................................................................B-1 

Appendix C. BART-Eligible Sources’ Stack Parameters, Base Elevation 

 at Ground Level and Stack Coordinates ...............................................C-1 

Appendix D. BART-Eligible Emission Rates used for the 

 No-obs Modeling Run.......................................................................... D-1 

Appendix E.Chapter 5 of the CENRAP BART Modeling Guidelines ......................E-1 

Appendix F.CALMET Input Control Files................................................................F-1 

Appendix G.CALPUFF Input Control Files............................................................. G-1 

Appendix H.POSTUTIL Input Control Parameters...................................................F-1 

Appendix I. CALPOST Input Control Parameters .....................................................I-1 

Appendix J. Chapter 3 of the CENRAP BART Modeling Guidelines.......................J-1 

 

 

ASHDOWN-Q8-000620
iii

List of Figures 

Figure 1 Mandatory Class I federal areas in the U.S.....................................................3 

Figure 2 Arkansas’ Class I areas ...................................................................................3 

Figure 3 Map showing the 300 km radius buffer zones around five separate 

 receptors (north, south, east, west, and center) located in the following 

 Class I areas: Upper Buffalo, Caney Creek, Hercules Glade, Mingo, and 

 Sipsey. This map was developed to determine which Class I areas will 

 Bee assessed during the BART determination modeling ...............................4 

Figure 4 Map indicating the locations of Upper Buffalo, Caney Creek, and the 

 eighteen BART-eligible facilities located in Arkansas...................................7 

Figure 5 Map indicating the locations of Upper Buffalo, Caney Creek, Hercules 

 Glade, Mingo, Sipsey, and the eighteen BART-eligible facilities 

 located in Arkansas.........................................................................................8 

Figure 6 Map of the 6 km CENRAP Central CALPUFF domain...............................12 

ASHDOWN-Q8-000621
iv

List of Tables 

Table 1 Class I areas and their locations as well as the supervising agencies 

 ADEQ will be assessing..................................................................................4 

Table 2 Fossil fuel-fired steam electric plants > 250 MMBtu/hr and Kraft 

 pulp mills facilities with BART-eligible emissions units...............................5 

Table 3 Petroleum refineries, sintering plants and chemical processing 

 plants facilities with BART-eligible emission units .......................................6 

Table 4 CALPUFF modeling components.................................................................13 

Table 5 Species modeled in BART screening analyses.............................................15 

Table 6 EPA recommended monthly average f(RH) for the five Class I areas 

 ADEQ are assessing......................................................................................17 

Table 7 Average annual natural levels of aerosol components (μg/m3

) ....................18 

ASHDOWN-Q8-000622
1 

I. Introduction 

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final 

amendments to its 1999 Regional Haze Rule in the Federal Register, including Appendix 

Y, the final guidance for Best Available Retrofit Technology (BART) determinations (70 

FR 39104-39172). The BART rule requires the installation of BART on emission sources 

that fit specific criteria and “may reasonably be anticipated to cause or contribute” to 

visibility impairment in any Class I area. Air quality modeling is the preferred method 

for establishing which emission sources cause or contribute to visibility impairment. 

Arkansas’ BART modeling protocol is provided herein. 

According to the Regional Haze Regulations and Guidelines for Best Available Retrofit 

Technology (BART) Determination; Final Rule (40 CFR Part 51, p 39125), each state is 

required to develop a BART Modeling Protocol that describes the required methodology 

to assess the levels of controls needed on sources subject to BART. The aforementioned 

regulation also requires states to work in partnership with all stakeholders including 

Tribes, EPA, Federal Land Managers (FLMs), Regional Planning Organizations (RPOs) 

and the various source operators. Although states are required to work in concert with 

the previously mentioned stakeholders, EPA has the ultimate authority to approve or 

disapprove a state’s State Implementation Plan (SIP). 

The main objective of this protocol is compliance with the RHR visibility improvement 

goals. To accomplish this goal, the Arkansas Department of Environmental Quality 

(ADEQ) has set forth three functions of this protocol. First, ADEQ will use the protocol 

to determine which BART-eligible units are subject-to-BART and must perform a 

BART-analysis. Second, facilities that ADEQ notifies that are subject-to-BART will use 

this protocol to conduct post-control modeling required for their BART-analysis. Third, 

the results from this protocol will be used to conduct cumulative modeling to show the 

change in visibility impact on Class I areas based on ADEQ’s BART determination and 

the BART emission limits for facilities based on their BART-analysis. The subject-toBART and final modeling will be submitted to the EPA as part of the BART section of 

the Arkansas State Implementation Plan (SIP) for Regional Haze. 

 

The AR RH SIP submittal deadline to EPA as set forth in the Regional Haze Regulations 

and Guidelines for Best Available Retrofit Technology (BART) Determination; Final 

Rule (40 CFR Part 51, p 39156) is December 17, 2007. To meet this deadline, ADEQ 

has developed a schedule for completing BART determinations and implementing the 

BART strategy in order to meet the mandatory SIP submittal deadline (Appendix A). As 

shown in Appendix A, the modeling results must be completed no later than March 1, 

2007. 

The Central States Regional Planning Association (CENRAP) contracted with Alpine 

Geophysics, LLC to develop a modeling protocol for the states within CENRAP’s region 

of which the state of Arkansas is a member. On December 22, 2005, Alpine Geophysics, 

LLC delivered the final version of the CENRAP BART Modeling Guidelines (Tesche, et 

ASHDOWN-Q8-000623
2 

al, 2005). However, comments from EPA Regions VI and VII and the Federal Land 

Managers (FLMs) were not incorporated into the guidelines; thus, Alpine Geophysics, 

LLC rewrote the guidelines to reflect the comments from Regions VI and VII and FLMs. 

These guidelines were re-issued February 3, 2006. Hence, CENRAP’s BART Modeling 

Guidelines (Tesche, et al, 2005) have been approved by Regions VI and VII and the 

FLMs. Therefore, the Planning and Air Quality Analysis Branch, Air Division, Arkansas 

Department of Environmental Quality has chosen to adopt the CENRAP BART 

Modeling Guidelines as ADEQ’s BART Modeling Protocol. Additionally, in preparing 

this draft protocol, ADEQ also consulted the following draft BART modeling protocols: 

 

1. Best Available Retrofit Technology (BART) Modeling Protocol to Determine 

Sources Subject to BART in the State of Kansas draft version February 24, 2006 

2. Best Available Retrofit Technology (BART) Modeling Protocol to Determine 

Sources Subject to BART in the State of Minnesota draft version February 24, 

2006 

This draft protocol is most similar to the CENRAP BART Modeling Guidelines. These 

guidelines were developed to ensure “consistency between states in the development of 

BART modeling protocols and to harmonize the approaches between adjacent RPOs” 

(Tesche, et al, 2005). 

Soon after the finalization of this modeling protocol, ADEQ will notify sources subjectto-BART. For those facilities subject-to-BART, ADEQ will provide guidance for 

conducting their BART-analyses.

II. Background 

The Clean Air Act Amendments (CAAA) of 1977 established 156 Class I areas where 

visibility was determined to be an important value (Figure 1). Areas designated as Class 

I areas are those national parks exceeding 6000 acres, wilderness areas and national 

memorial parks exceeding 5000 areas, and all international parks that were in existence 

on August 7, 1977. While Rainbow Lake Wilderness Area, Wisconsin has been 

designated as a Class I area, the FLMs have indicated that visibility is not a valuable 

characteristic and therefore, is not included in BART or other RH analyses. 

The state of Arkansas has within her boundary two mandatory Class I federal areas (Class 

I area), Upper Buffalo Wilderness Area and Caney Creek Wilderness Area which are 

managed by the United States Forest Service (Figure 2). However, there are two Class I 

areas in southern Missouri that are located downwind of facilities operating in Arkansas. 

The Missouri Class I areas are Hercules-Glade Wilderness Area (US Forest Service) and 

Mingo National Wildlife Refuge (US Fish and Wildlife). While EPA has not listed the 

maximum distance from a Class I area to model, this criteria has been set by CENRAP as 

300 km. As shown in Figure 3, the eastern portion of Arkansas is within the 300 km 

radius of Sipsey Wilderness Area (US Forest Service), Alabama. Therefore, there are 

ASHDOWN-Q8-000624
3 

five Class I areas Arkansas will be performing BART determination/exemption modeling 

(Table 1). 

Figure 1 Mandatory Class I federal areas in the United States of America 

Figure 2 Arkansas’s Class I areas 

ASHDOWN-Q8-000625
4 

Figure 3 Map showing the 300 km radius buffer zones around five separate receptors 

(north, south, east, west, and center) located in the following Class I areas: Upper 

Buffalo, Caney Creek, Hercules Glade, Mingo, and Sipsey. This map was developed to 

determine which Class I areas will be assessed during the BART determination modeling 

Table 1 Class I areas and the State they are located in as well as the supervising agencies 

ADEQ will evaluate during the BART determination/exemption modeling 

Class I Area State Supervising Agency 

Upper Buffalo Wilderness Area AR U.S. Forest Service 

Caney Creek Wilderness AR U.S. Forest Service 

Hercules Glade Wilderness Area MO U.S. Forest Service 

Mingo NWS MO U.S. Fish and Wildlife

Sipsey Wilderness Area AL U.S. Forest Service 

III. BART-Eligible Sources 

The BART requirements in the RHR are intended to reduce emissions specifically from 

large emission units that, due to age, were exempted from other control requirements of 

the CAAA. For an emissions unit to be considered eligible for BART, it must fall into 

one of 26 specified categories, must have the potential to emit at least 250 tons per year 

of certain haze-forming pollutants, and must have been in existence on August 7, 1977, 

but not in operation before August 7, 1962. 

ADEQ staff determined Arkansas’ BART-eligible sources by first identifying which of 

Arkansas’ stationary sources fit the first criteria of being listed in the BART 26 specific 

categories. After identifying the sources which fit the first criteria, a database search of 

ASHDOWN-Q8-000626
5 

these facilities was performed to determine whether or not these emitting units’ potential 

to emit were at least 250 tons per year of sulfur dioxide (SO2), nitrogen oxides (NOx), and 

particulate matter (PM). The next stage of determining BART-eligibility was to research 

the permit applications for the year the point source was placed into operation. The final 

step in the process was to contact facilities for the exact date of operation especially for 

sources that were placed into operation in the years 1977 and 1962. Tables 2 and 3 

contain the list of BART-eligible facilities (18) by BART source category and the 

number of BART-eligible emitting units (27) within each facility. Figure 4 is a map of 

Arkansas which shows the location of the 18 BART-eligible facilities located in 

Arkansas. Figure 5 depicts the five Class I areas Arkansas will be assessing and the 

BART-eligible sources in Arkansas. Appendix B contains maps showing the receptors at 

each Class I area ADEQ will be assessing. 

 

Table 2 Fossil fuel-fired steam electric plants > 250 MMBtu/hr and Kraft pulp mills 

facilities with BART-eligible emission units 

BART Source Category 

Number and Name 

Facility 

Name/Location 

Facility 

ID 

AFIN Unit 

ID 

Unit 

Description 

American Electric Power 

(SWEPCO)/Gentry 

05-007-

00107 

04-

0017 

SN-01 Boiler 

AR Electric 

Cooperative/Augusta 

05-147-

00024 

74-

00024 

SN-01 Boiler 1350mm 

AR Electric 

Cooperative/Camden 

05-103-

00055 

52-

00055 

SN-01 Boiler 

Entergy – Lake 

Catherine/Jones Mill 

05-059-

00011 

30-

00011 

SN-03 Unit 4 Boiler 

Entergy – Ritchie 

Plant/Helena 

05-107-

00017 

54-

00017 

SN-02 Unit 2 

Entergy – White Bluff/ 

Redfield 

05-069-

00110 

35-

00110 

SN-01 Unit 1 

Entergy – White 

Bluff/Redfield 

05-069-

00110 

35-

00110 

SN-02 Unit 2 

1. Fossil fuel-fired Electric 

Plants > 250 MMbtu/hour – 

Electric Generating Units 

(EGUs) 

Entergy – White 

Bluff/Redfield 

05-069-

00110 

35-

00110 

SN-05 Auxiliary Boiler 

Domtar, Inc./Ashdown 05-081-

00002 

41-

00002 

SN-03 #1 Power Boiler 

Domtar, Inc./Ashdown 05-081-

00002 

41-

00002 

SN-05 #2 Power Boiler 

Delta Natural Kraft/Pine 

Bluff 

05-069-

00017 

35-

00017 

SN-02 Recovery Boiler 

Georgia – Pacific 

Paper/Crossett 

05-003-

00013 

02-

00013 

SN-22 9A Boiler 

Green Bay Packing/ 

Morrilton 

05-029-

00001 

15-

00001 

SN05A Recover Boiler 

3. Kraft Pulp Mills 

Potlatch/McGehee 05-041-

00036 

21-

00036 

SN-04 Power Boiler 

ASHDOWN-Q8-000627
6 

Table 3 Petroleum refineries, sintering plants and chemical processing plant facilities 

with BART-eligible emissions units 

BART Source 

Category Number 

and Name 

Facility 

Name/Location 

Facility 

ID 

AFIN Unit 

ID 

Unit 

Description 

11. Petroleum 

Refineries 

Lion Oil/El Dorado 05-139-

00016 

70-

00016 

SN809 #7 Catalyst 

Regenerator 

19. Sintering Plants Big River Industries 

/West Memphis 

05-035-

00082 

198-

00082 

SN-01 Kiln A 

Albermarle – South 

Plant/Magnolia 

05-027-

00028 

14-

00028 

SR-01 Tail Gas 

Incinerator 

Albermarle – South 

Plant/Magnolia 

05-027-

00028 

14-

00028 

BH-01 Boiler #1 

Albermarle – South 

Plant/Magnolia 

05-027-

00028 

14-

00028 

BH-02 Boiler #2 

Eastman 

Chemical/Batesville 

05-063-

00036 

32-

00036 

6M01-

01 

3 Coal Boilers 

El Dorado Chemical/El 

Dorado 

05-139-

00040 

70-

00040 

SN-08 West Nitric Acid 

Plant 

El Dorado Chemical/El 

Dorado 

05-139-

00040 

70-

00040 

SN-09 East Nitric Acid 

Plant 

21. Chemical 

Processing Plants 

El Dorado Chemical/El 

Dorado 

05-139-

00040 

70-

00040 

SN-10 Nitric Acid 

Concentrator 

ASHDOWN-Q8-000628
7 

Figure 4 Map indicating the locations of Upper Buffalo, Caney Creek and the eighteen 

BART-eligible facilities located in Arkansas 

ASHDOWN-Q8-000629
8 

Figure 5 Map indicating the locations of Upper Buffalo, Caney Creek, Hercules Glade, 

Mingo and the eighteen BART-eligible facilities located in Arkansas 

IV. CAIR and Arkansas 

The Clean Air Interstate Rule was finalized in May 2005 by EPA and applies to states in 

the eastern U.S. Reconsiderations were finalized March 2006. This rule address air 

pollution transport across state borders. EPA determined which states must reduce which 

pollutants based on modeling which showed how the travel of pollution affects nonattainment in other states. CAIR requires states to reduce NOx and/or SO2 emissions. Of 

the three programs in CAIR, Arkansas is required to participate in only the Ozone-Season 

NOx reductions program. Although EPA’s BART Modeling Guidance allows CAIR 

states to participate in the CAIR cap and trade program, the state of Arkansas is not 

eligible for the aforementioned trading program because Arkansas is in CAIR only for 

NOx during the ozone season. Therefore, in Arkansas CAIR is not better than BART. 

Thus BART-eligible EGUs will be modeled for BART determination/exemption by 

ADEQ. 

V. BART Air Quality Modeling Approach 

According to EPA’s BART Modeling Guidance, “CALPUFF is the best regulatory 

modeling application currently available ... and is currently the only EPA-approved 

model...” (p 45); therefore, ADEQ and CENRAP have chosen to use CALPUFF in the 

ASHDOWN-Q8-000630
9 

BART determination process as well as in the post-control analysis. One of the air 

quality modeling approaches suggested by EPA in the BART guidance is an individual 

source attribution approach. This is the approach ADEQ proposes to take. Specifically, 

this entails modeling source-specific units and comparing modeled impacts to a particular 

deciview threshold (described below). ADEQ has decided to conduct the subject-toBART modeling, rather than have each BART-eligible facility either conduct the 

modeling or hire a contractor. This plan will eliminate the need for ADEQ to quickly 

review many air quality modeling analyses conducted using varying approaches. This 

plan will also satisfy the need to use a consistent approach among the modeling analyses. 

Once the subject-to-BART modeling is complete, all the modeling inputs will be 

available to facilities subject to BART for them or their consultants to conduct modeling 

for making BART analyses. 

ADEQ will follow EPA’s BART Modeling Guidance (p 42) in sitting a threshold limit in 

determining whether a BART-eligible source is either subject-to-BART or exempt. 

According to the aforementioned modeling guidance, an individual source will be 

considered to “cause visibility impairment” if the emissions results in a change (delta ∆) 

in deciviews (dv) that is greater than or equal to 1.0 deciview on the visibility in a Class I 

area. Additionally, if the emissions from a source results in a change in visibility that is 

greater than or equal to 0.5 dv in a Class I area the source will be considered to 

“contribute to visibility impairment” (BART Final Rule, 40 CFR 51 p 39113). Thus, 

ADEQ has set the threshold limit at 0.5 dv. 

The modeling approach discussed here is specifically designed for conducting the 

subject-to-BART screening analyses. There may be differences between modeling for 

conducting BART analyses and that for conducting a visibility analysis for a New Source 

Review permit, which may involve similar emission sources and the same air dispersion 

model used here. 

To ensure that no sources pass the screening test when they should fail, the simple 

approach, by its nature, must be the most conservative of all the conditions likely to be 

examined for the source in question. For example, many factors influence the 

contribution of a source to the Class I area other than distance. The frequency of winds 

transporting the pollutants toward the Class I area may often be important to include for a 

reliable screening analysis. Also, a more distant Class I area downwind in the 

predominant wind direction from a source may receive a higher visibility impact than a 

closer Class I area that is infrequently downwind of the source. Another example of 

conservatism in the screening process is the use of the latest beta version of the 

CALMET/CALPUFF modeling system using the no-observation (no-obs) mode (the 

prognostic meteorological model MM5). Thus, the maximum impact instead of the 98th

percentile will be used to determine if a source has an impact on visibility in a Class I. 

Additionally, the BART analysis process includes several other steps in addition to the 

modeling described in this protocol (EPA, 2005). These steps, none of which are 

addressed in this document, include detailed analysis of: 

ASHDOWN-Q8-000631
10 

 Costs of compliance among the various retrofit control options 

 Energy and non-air quality impacts 

 Existing pollution control technologies in use at the BART-eligible unit 

particularly with respect to their affecting the choice of retrofit options 

 Remaining useful life of the units and/or facility 

 Improvements in visibility expected from the use of BART controls. 

VI. BART-Eligible Units Physical Parameters 

The physical characteristics of the BART-eligible point sources to be used for the 

screening stage one analysis will be provided by ADEQ staff. For the stage two 

screening analysis, ADEQ staff will work with the BART-eligible facilities in the 

development of actual emissions. 

A. Stack Parameters 

Stack parameters required for modeling BART-eligible units were extracted from the 

permit applications. Stack parameters include height of the stack opening from ground in 

meters, inside diameter in meters, exit velocity in meters per second, exit gas temperature 

in Kelvin, ground elevation of the stack base in meters, and location coordinates of the 

stack in Lambert Conformal Conical (LCC). The stack coordinates were taken (in 

Universal Transverse Mertcator, UTM, and then converted to LCC) by ADEQ staff and 

then verified using ArcMap. Because the BART modeling focuses on mesoscale 

transport to Class I areas, other source term parameters (needed to calculate localized 

impacts) such as building heights and widths for calculating downwash will not be used. 

Appendix C contains tables indicating the stack parameters and coordinates for each 

BART-eligible emitting unit. 

B. Emission rates 

ADEQ notified by email the BART-eligible facilities to provide the 24-hour average 

actual emission rate with normal operations from the highest emitting day of the year. 

Excluded from consideration are days where start-up, shutdown or malfunctions occurred 

unless these activities are regular, frequently occurring components of the source’s 

operation cycle. 

ADEQ does not intend to use emissions of VOCs and ammonia from facilities for 

subject-to-BART analysis. Only specific VOC compounds form secondary organic 

aerosols that affect visibility. These compounds are a fraction of the total VOCs reported 

in the emissions inventory, and ADEQ does not have the breakdown of VOC emissions 

necessary to model those that only impair visibility. Further, the prescribed screening 

model (CALPUFF) cannot simulate formation of particles from anthropogenic VOCs, 

nor their visibility impacts. Ammonia from specific sources will not be evaluated in this 

process, although ammonia is included in the modeling as a background concentration—

this will be discussed later in this modeling protocol. The appropriate VOCs and 

ASHDOWN-Q8-000632
11 

ammonia emission data can, and will be, included in regional scale modeling used for the 

Regional Haze SIP. 

VII. Air Quality Model and Inputs 

As stated in the previous section, CALPUFF is the preferred regulatory air dispersion 

model for long distance and therefore is the model ADEQ will be using in the BART 

determination process. ADEQ recognizes that CALPUFF has limited ability to simulate 

the complex atmospheric chemistry involved in the estimation of secondary particulate 

formation. However, for purposes of the subject-to-BART analysis, ADEQ intends to use 

CALPUFF for the following reasons: 

1. The increased level of effort required for conducting particulate apportionment in 

the regional scale, full-chemistry Eulerain model (CAMx) to acquire individual 

source contributions to Class I areas, relative to the simplicity of the CALPUFF 

model 

2. The lack of a plume-in-grid feature with the particulate apportionment technique 

currently available in CAMx 

3. The desire to be consistent with other CENRAP states, which all (except Texas 

and Iowa) appear to be using CALPUFF 

4. The limited scope of what this modeling is to determine 

5. The additional modeling of BART controls that will be conducted as part of the 

Regional Haze SIP with the CAMx or CMAQ model(s). EPA’s BART guidance 

states that States should follow the EPA’s Interagency Workgroup on Air Quality 

Modeling (IWAQM) guidance, Phase 2 recommendations for long-range 

transport. The IWAQM guidance was developed to address air quality impacts as 

assessed through the Prevention of Significant Deterioration (PSD) program at 

Class I areas, where the source generally is located beyond 50 km of the Class I 

area. The IWAQM guidance does not specifically address the type of assessment 

that will occur with the BART analysis. 

EPA recommends in their BART modeling guidelines (2005) that States follow the 

Interagency Workgroup on Air Quality Modeling (IWAQM) Phase II (1998) for longrange transport. The IWAQM guidance was developed to address air quality impact – as 

assessed through the Prevention of Significant Deterioration (PSD) program – at Class I 

areas, where the source generally is located beyond 50 km of the Class I area. The 

IWAQM guidance does not specifically address the type of assessment that will occur 

with the BART modeling. 

ASHDOWN-Q8-000633
12 

A. CALPUFF Screening Modeling: 

CALPUFF modeling will be performed on all Arkansas BART-eligible sources. ADEQ 

intends to closely follow the CENRAP BART modeling protocol for most of the settings 

and inputs. Kansas attempted puff splitting and found this method to be computationally 

prohibitive on the current domain (State of Kansas, 2006). Also, according to Tesche, et 

al (2005), 

“There is no quantitative evidence that the horizontal and vertical puff-splitting 

algorithms in CALPUFF yield improved accuracy and precision in model 

estimates of inert or linearly reactive pollutants although conceptually the 

methods have appeal in that they attempt to mimic lateral and vertical wind 

speed and direction shears.” (p 6-6) 

Therefore, ADEQ will not invoke puff splitting in the no-obs screening analysis nor in 

the refined screening analysis. However, if a potentially subject-to-BART facility wishes 

to invoke the puff splitting mode, they will be required to notify ADEQ in writing of their 

intent and provide a protocol for approval prior to performing the analysis. 

1. Modeling domain 

The CALPUFF modeling will be conducted on the CENRAP central 6 km grid. The 

extent of the proposed CALPUFF domain is shown in Figure 2. 

Figure 6 6 km CENRAP Central CALPUFF domain (Tesche, et al, 2005) 

ASHDOWN-Q8-000634
13 

CALPUFF will be applied to each source for three annual simulations spanning the years 

2001 through 2003. The IWAQM Phase II guidance allows the use of fewer than 5 years 

of meteorological data if a meteorological model using four-dimensional data 

assimilation is used to supply data. This is the case in this modeling analysis. See the 

section on meteorology for more information. 

2. CALPUFF system implementation 

There are three main components to the CALPUFF model: 

1. Meteorological Data Modeling (CALMET); 

2. Dispersion Modeling (CALPUFF); and 

3. Post-processing (CALPOST) 

Versions of the modeling components to use in this BART analysis are shown in Table 4. 

Table 4 CALPUFF Modeling Components 

Processor Version Level 

TERREL 3.311 030709 

CTGCOMP 2.42 030709 

CTGPROC 2.42 030709 

MAKEGEO 2.22 030709 

CALMM5 2.4 050413 

CALMET 5.53a 040716 

CALPUFF 5.753 051130 

POSTUTIL 1.4 040818 

CALPOST 5.6392 051130 

The specific use of each of these components in the BART analysis is described in more 

detail below. 

For screening applications, ADEQ will use the VISTAS version which is the latest ‘beta’ 

versions of the CALMET/CALPUFF modeling system. Note that these are not the EPA 

guideline codes but rather an updated version containing recent (as of this writing) 

science improvements and bug fixes. The current guideline CALPUFF code is version 

5.7, level 030402. This substitution results from EPA phasing out the use of the legacy 

Pasquill-Gifford (P-G) dispersion parameters with the introduction of AERMOD as a 

new guideline model. CALPUFF employs the AERMOD turbulence-based dispersion 

coefficients and probability density function (pdf) dispersion methods scheme instead of 

P-G. 

The appropriate model codes may be downloaded from www.src.com or purchased with 

the latest graphical user interface (GUI) from the model developer. The sequence of 

model processors listed in Table 4 corresponds to the order in which the programs are 

typically run. 

3. Meteorological data modeling (CALMET) 

ADEQ will use the 2001-2003 CENRAP developed no-obs CALMET dataset for the 

screening analysis.* This decision was based on EPA Regions VI and VII written 

comments on the CENRAP BART Modeling Guidelines (Tesche, et al, 2005) which 

state, 

ASHDOWN-Q8-000635
14 

“Normally, in accordance with Section 8.3.1.2 (d) of the Guideline on Air Quality 

Models, the EPA would require that observations be incorporated in conjunction 

with prognostic meteorological data. While the idea of use of prognostic data 

alone holds promise, it is our opinion that this option requires further evaluation 

to insure that this approach does not bias CALPUFF towards underestimation 

(Guideline on Air Quality Models, Section 3.2.2 (d)(iv)). While we have 

significant concern regarding the use of the CALMET fields as they have been 

developed under the procedures documented in this protocol, we would consider 

the use of the CALMET meteorological fields provided the screening 

methodology described in Section 6.1 of the protocol is strictly adhered to. In this 

case, we feel that the use of the maximum visibility impact rather than the 98th

percentile value is conservative in its application, and would overcome concerns 

of a potential bias towards underprediction [sic] of the “no-observation” mode. 

Under these circumstances, we would consider the use of the CALMET fields 

acceptable for the CALPUFF screening procedure.” (EPA, 2005)

As stated in Section V. BART Air Quality Modeling Approach, ADEQ will use the 

maximum impact instead of the 98th percentile to determine if a source has an impact on 

visibility in a Class I. 

However, subject-to-BART facilities have the option of using the CENRAP CALMET 

processed data or incorporating observational meteorological data into the 

aforementioned CALMET data. If a subject-to-BART facility opts to use the CENRAP 

CALMET processed data, then the facility will be required to use the maximum impact 

instead of the 98th percentile (8th day). If a subject-to-BART facility decides they would 

rather use the 98th percentile, then the facility will be required to incorporate 

observational data and provide a protocol as well as a performance evaluation which will 

need to be approved by ADEQ, EPA, and the FLMs. 

Appendix F contains the detailed information on all CALMET setting that was used to 

develop the post-processed no-obs data fields. 

4. Dispersion modeling (CALPUFF) 

The CALMET output is used as input to the CALPUFF model, which simulates the 

effects of the meteorological conditions on the transport and dispersion of pollutants from 

an individual source. In general, ADEQ proposes to use the recommended default options 

in the CALPUFF model. There are some deviations, which are discussed below. Table 5 

indicates the species that will be modeled and/or emitted in the no-obs and refined BART 

analyses. 

ASHDOWN-Q8-000636
15 

Table 5 Species modeled in BART screening analyses 

Species Modeled Emitted Dry Deposited 

SO2 Yes Yes Computed-gas 

SO4

-2 Yes No Computed-particle

NOx Yes Yes Computed-gas 

HNO3 Yes No Computed-gas 

NO3

-

 Yes No Computed-particle

PM-fine* Yes Yes Computed-particle

PM10* Yes Yes Computed-particle

*Please refer to Section VI subsection B for a detailed discussion on PM-fine and PM10. 

Emissions Speciation: ADEQ does not intend to model sulfate (SO4

-2), nitrate (NO3-

), 

elemental carbon (EC), and secondary organic aerosols (SOA) during the screening 

analyses. However, ADEQ recognizes the impact EC and SOA have on visibility. For 

instance, the light extinction (βext) coefficient for EC is 10 and for SOA it is 4. Currently, 

data are quite limited on appropriate speciation of organic/inorganic and 

filterable/condensable emissions by source category. Although there are speciation 

profiles available for gas- and oil-fired combustion turbines and coal combustion 

processes, currently there are no detailed profiles for the full range of BART-eligible 

sources. Thus, in the case of a subject-to-BART source where the PM profile for SO42-, 

EC, and SOA are known, ADEQ recommends the aforementioned species be modeled as 

separate species in CALPUFF in the post-control modeling analysis. 

Condensable Emissions: According to Tesche, et al (p 6-5 2005), “condensable 

emissions are considered primary fine particulate.” ADEQ is aware of the inability to 

measure PM2.5 emissions. Thus, BART-eligible facilities will be most likely use AP-42 

emission factors to develop the “actual” highest average 24-hr emission rate for this 

pollutant. In the development of this emission rate, ADEQ will require these facilities to 

use the AP-42 emission factors for condensable PM2.5. For sources where AP-42 factors 

are not available, assumptions for partitioning need to be resolved with ADEQ. 

Size Classification of Primary PM Emissions: Particle size parameters are entered in the 

CALPUFF input file for dry deposition of particles. There are default values for “aerosol” 

species (i.e., SO4

-2, NO3-

, and PM2.5). The default value for each of these species is 0.48 

μm geometric mass mean diameter and 2.0 μm geometric standard deviation. The main 

sources of these particles are fuel combustion. A way to account for this, without 

including EC and SOA in the modeling, is to use particle speciation in the postprocessing step. This is discussed below in the CALPOST section. 

As stated in a previous section, all PM10 emissions will be modeled as PM2.5 for the noobs model simulations (Tesche, et al, 2005). 

Background Ozone concentrations: Ozone (O3) can be input to CALPUFF as hourly or 

monthly background values. Hourly values of ozone concentrations were obtained from 

two rural monitoring sites in Arkansas: Deer, Newton County monitoring site and Eagle 

Mountain, Montgomery County monitoring site. The hourly ozone concentrations were 

adjusted for the time differences between the post-processed prognostic meteorological 

ASHDOWN-Q8-000637
16 

file (0 GMT) and the collection time of the ozone (LST). Also, the concentrations were 

adjusted from parts per million (ppm) to parts per billion (ppb). These hourly ozone 

values will be used in this modeling. 

Background Ammonia concentrations: Background ammonia concentration is assumed 

to be temporally and spatially invariant and will be fixed at 3 ppb across the entire 

domain for all months. It may be possible to derive NH3 concentrations from regional 

modeling outputs that CENRAP is currently developing. At this time these NH3 values 

are not available in a model ready form. 

Receptors: Receptors are locations where model results are calculated and provided in the 

CALPUFF output files. Receptor locations were derived from the National Park Service’s 

Class I area receptor database at 

http://www2.nature.nps.gov/air/maps/receptors/index.cfm. Only these discrete NPS 

receptors will be modeled in CALPUFF. The discrete receptors are necessary for 

calculating visibility impacts in the nine selected Class I areas that will be evaluated by 

ADEQ. All the discrete receptors will be placed with enough density that the highest 

visibility impacts should be evident. The NPS provides receptors in all the Class I areas 

on a 1 km basis. These receptors will be kept at the 1 km spacing for the BART 

modeling, and all receptors will be retained. NPS also provides a conversion program to 

convert the coordinates of the receptors from latitude/longitude (lat/long) to Lambert 

Conformal Conical (LCC). ADEQ used this conversion program to convert the receptors 

located in the five Class I areas it is assessing from lat/long to LCC. 

Outputs: The CALPUFF modeling results will be displayed in units of micrograms per 

cubic meter (μg/m3

). In order to determine visibility impacts, the CALPUFF outputs must 

be post-processed. 

Detailed information on all CALPUFF setting to be used in this screening analysis is 

located in Appendix G. 

5. Post-processing (POSTUTIL/CALPOST) 

Hourly concentration outputs from CALPUFF are processed through POSTUTIL and 

CALPOST to determine visibility conditions. Specifically, POSTUTIL takes the 

concentration file output from CALPUFF and recalculates the nitric acid and nitrate 

partition based on total available sulfate and ammonia. The ammonia-limiting method 

(ALM) in CALPUFF repartitions nitric acid and nitrate on a receptor-by-receptor and 

hour-by-hour basis to account for the models systematic over-prediction due to 

overlapping puffs. For both screening applications, the parameter MNIRATE=1 is set in 

POSTUTIL to implement this approximate correction in its simplest form. The 

background ammonia concentration that was obtained from CENRAP’s regional 

modeling effort will be used to maintain regional consistency in the CENRAP region. 

CALPOST uses the concentration file processed through POSTUTIL, along with relative 

humidity (RH) data, to perform visibility calculations. For the BART analysis, the only 

modeling results out of the CALPUFF modeling system of interest are the visibility 

impacts. 

ASHDOWN-Q8-000638
17 

Please see Appendix H and I for detailed settings for POSTUTIL and CALPOST. 

Light extinction: Light extinction must be computed in order to calculate visibility. 

CALPOST has seven methods for computing light extinction. This BART screening 

analysis will use Method 6, which computes extinction from speciated particulate matter 

with monthly Class I area-specific relative humidity adjustment factors, and is implied by 

the BART guidance. Relative humidity (RH) is an important factor in determining light 

extinction (and therefore visibility) because SO4

-2 and NO3-

 aerosols, which absorb 

moisture from the air, have greater extinction efficiencies with greater RH. All BART 

analyses will apply relative humidity correction factors (f(RH)s) to SO4

-2 and NO3-

concentrations outputs from CALPUFF, which were obtained from EPA’s “Guidance for 

Estimating Natural Visibility Conditions under the Regional Haze Rule (EPA, 2003). The 

f(RH) values for the Class I areas that will be assessed are provided in Table 5. 

Table 6 EPA recommended monthly averaged f(RH) for the five Class I areas ADEQ is 

assessing (EPA, 2003) 

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Caney Creek 3.4 3.1 2.9 3.0 3.6 3.6 3.4 3.4 3.6 3.5 3.4 3.5 

HerculesGlades 3.2 2.9 2.7 2.7 3.3 3.3 3.3 3.3 3.4 3.1 3.1 3.3 

Mingo 3.3 3.0 2.8 2.6 3.0 3.2 3.3 3.5 3.5 3.1 3.1 3.3 

Sipsey 3.3 3.0 2.8 2.7 3.1 3.4 3.5 3.5 3.5 3.3 3.1 3.3 

Upper Buffalo 3.3 3.0 2.7 2.8 3.4 3.4 3.4 3.4 3.6 3.3 3.2 3.3 

The PM2.5 concentrations are considered part of the dry light extinction equation and do 

not have a humidity adjustment factor. The light extinction equation is the sum of the wet 

SO4

-2 and NO3-

 and dry components PM2.5 plus Rayleigh scattering (βRay), which is 10 

inverse megameters (Mm-1). 

To account for sources modeled with a known PM speciation profile for EC, SOA, and 

SO4, an adjustment to the extinction coefficient for the PM components will be made in 

CALPOST. ADEQ intends to follow the method outlined in the FLM CALPUFF 

Reviewer’s Guide (Gebhart, 2005) which is located in Appendix K. 

6. Measuring visibility impacts 

The recommended procedure for quantifying visibility impacts can be found in Chapter 3 

of the CENRAP BART Modeling Guidelines (Tesche, et al, 2005) which is located in 

Appendix J. The key point is that the light extinction coefficient (βext) can be calculated 

from the IMPROVE equation as: 

 βext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] + 

 + 0.6[Coarse Mass] + 10[EC] + βRay

ASHDOWN-Q8-000639
18 

The monthly site-specific f(RH) values were obtained for the five Class I Area ADEQ is 

assessing from Table A-3 in the EPA (2003) guidance document. Then, the haze index 

(HI), in dv, is calculated in terms of the extinction coefficient via: 

HI = 10 ln (βext/10) 

The change in visibility (measured in terms of ∆ dv) is then compared against 

background conditions. The ∆ dv value is calculated from the source’s contribution to 

extinction, βsource, and background extinction, βbackground, as follows:

 

∆ dv = 10 ln ({βbackground+ βsource}/ βbackground) 

If the ∆ dv value is greater than or equal to 0.5 dv, the source is said to contribute to 

visibility impairment and is thus subject-to-BART controls. If not, it is BART-exempt. 

The annual average natural levels of aerosol components at each Class I area being 

evaluated by ADEQ are shown in Table 7. Natural conditions by component in Table 6 

are based on whether the Class I area is in the eastern or the western part of the United 

States. In this BART analysis, all Class I areas are located in the East. The source of this 

data is from EPA’s Guidance for Estimating Natural Visibility Conditions Under the 

Regional Haze Rule (EPA, 2003). 

Table 7 Average annual natural levels of aerosol components (μg/m3

) (EPA, 2003) 

Class I Area Region SO4 NO3 OC EC Soil Coarse Mass

Caney Creek East 0.23 0.10 1.40 0.02 0.50 3.00

Hercules-Glades East 0.23 0.10 1.40 0.02 0.50 3.00

Mingo East 0.23 0.10 1.40 0.02 0.50 3.00

Sipsey East 0.12 0.10 0.47 0.02 0.50 3.00 

Upper Buffalo East 0.23 0.10 1.40 0.02 0.50 3.00

As stated in section V, in a cooperative agreement with EPA Regions VI and VII and 

FLMs, CENRAP guidance deviates from use of the 98th percentile impact. The 

CALMET datasets as described in this protocol were processed with the no-obs options 

(i.e., surface observations were not used in the CALMET wind field interpolation). 

Aware that exercising CALMET with no-obs may lead in some applications to 

potentially less conservatism in the CALPUFF visibility results compared with the use of 

CALMET with observations, CENRAP has agreed to EPA’s recommendation that the 

maximum visibility impact, rather than the 98th percentile value, should be used for the 

no-obs screening analysis using the CENRAP-developed CALMET datasets. 

If the no-obs screening analysis results indicate a BART-eligible facility’s maximum ∆

dv on a Class I area is less that 0.5 dv, then they will be considered exempt from BART 

and will be notified by ADEQ of their status. However, if the maximum ∆ dv is equal to 

or greater than 0.5 dv, the source will be considered to be subject-to-BART. ADEQ will 

notify these subject-to-BART facilities. 

ASHDOWN-Q8-000640
19 

VIII. Change in Visibility Due to BART Controls 

Once a facility is determined to be subject-to-BART, this facility must perform an 

engineering analysis and a post-control modeling analysis using CALPUFF. This 

modeling analysis must be compared to the pre-control modeling results. Please note that 

this will be a source specific (i.e. emitting unit specific) and pollutant specific modeling 

analysis using CALPUFF. If a subject-to-BART facility opts to use the 98th percentile 

rather than the maximum impact, the subject-to-BART facility will be required to be 

incorporate observational data with the post processed CALMET prognostic 

meteorological data. Also these facilities will be required to submit their meteorological 

modeling protocol, model performance evaluation, and CALPUFF modeling protocol to 

ADEQ, EPA Region VI, and FLMs for approval. However, if the subject-to-BART 

facility opts to use the maximum impact rather than the 98th percentile, these facilities 

may use the post-processed CALMET MM5 data. 

Additionally, one control measure that a source may opt to use is to revise their Title V 

permit to provide for synthetic minor limits so that it falls under the BART emission cap. 

That permit modification must be done prior to the State going to public hearing on its 

RH SIP. The limits must be in place for as long as the RH SIP is applicable or for as long 

as the source is operational. However, the source will still need to do a post-control 

CALPUFF modeling analysis to determine the amount of emissions it needs to reduce for 

visibility improvement. (Note: ADEQ strongly recommends that all subject-to-BART 

facilities work closely with ADEQ in their engineering analyses.) Also, after all of the 

post-control results are submitted to and approved by ADEQ, these results will then be 

inputted into either CAMx or CMAQ for a cumulative model run. If the control 

measures proposed by the BART facilities still impact a Class I area, the BART facilities 

will need to implement additional control. Please note that all post-modeling results are 

due to ADEQ no later than October 23, 2006. 

 

ASHDOWN-Q8-000641
20 

IX. References 

Gebhart, Howard (2005). Draft CALPUFF REVIEWER’S GUIDE. Air Resource 

Specialists, Inc., Fort Collins, Colorado. 

Kansas Department of Health and Environment, Bureau of Air and Radiation (March, 

2006). Best Available Retrofit Technology (BART) Modeling Protocol to Determine 

Sources Subject to BART in the State of Kansas

Minnesota Pollution Control Agency (2005). Best Available Retrofit Technology 

(BART) Modeling Protocol to Determine Sources Subject to BART in the State of 

Minnesota. 

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino. (2000). A User’s Guide for the

CALPUFF Dispersion Model (Version 5). Earth Tech, Inc., Concord, Massachusetts. 

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino. (2002). A User’s Guide for the

CALMET Dispersion Model (Version 5). Earth Tech, Inc., Concord, Massachusetts. 

Tesche, T.W, et al (2005) CENRAP BART Modeling Guidelines. Alpine Geophysics, 

LLC. Ft. Wright, KY 

U.S. EPA. (1998). Interagency Workgroup on Air Quality Modeling

(IWAQM) Phase2—Summary Report and Recommendations for Modeling Long Range 

Transport Impacts. EPA-454/R98-019. 

U.S. EPA (2003). Regional Haze: Estimating Natural Visibility Conditions 

Under the Regional Haze Rule. EPA-454/B-03-005. 

U.S. EPA. (2005). Regional Haze Regulations and Guidelines for Best Available 

Retrofit Technology (BART) Determinations. Federal Register Vol. 70, No. 128. 

U.S. EPA, Regions VI & VII (2005). BART Protocol Comments. 

U.S. EPA, Region VI (2006). Personal communication. 

ASHDOWN-Q8-000642
A-1 

Appendix A. Proposed Time-line 

Figure A-1 ADEQ’s proposed time-line to meet the RH SIP deadline of December 17, 

2007 as set forth by EPA in its Regional Haze Regulations and Guidelines for Best 

Available Retrofit Technology (BART) Determination (40 CFR Part 51, p 39156) 

ASHDOWN-Q8-000643
B-1 

Appendix B. Map of receptors 

Figure B-1 Receptors located in Caney Creek Wilderness Area, Arkansas 

Figure B-2 Receptors located in Hercules-Glade Wilderness Area, Missouri 

ASHDOWN-Q8-000644
B-2 

Figure B-3 Receptors located in Mingo Wilderness Area, Missouri 

Figure B-4 Receptors located in Sipsey Wilderness, Alabama 

ASHDOWN-Q8-000645
B-3 

Figure B-4 Receptors located in Upper Buffalo Wilderness Area, Arkansas 

ASHDOWN-Q8-000646
C-1 

Appendix C. BART-Eligible Sources’ Stack Parameters, Base 

Elevation at Ground Level, and Stack Coordinates 

Table C-1 BART-eligible sources’ stack parameters 

STATIONARY SOURCE 

NAME/LOCATION 

Emission 

Unit ID 

Stack 

Height 

Meters 

Stack 

Diameter

Meters 

Exit 

Velocity 

m/sec 

Temperature

o

K 

Albermarle-South Plant / Magnolia SR-01 57.9 0.814 15.24 922 

Albermarle-South Plant / Magnolia BH-01 6.4 2.591 9.14 505 

Albermarle-South Plant / Magnolia BH-02 6.4 2.591 9.14 505 

American Elect. Power (SWEPCO) / 

Gentry SN-01 164.6 6.096 34.14 408 

AR Elect. Coop - Bailey Plant / Augusta SN-01 50.9 3.000 28.04 444 

AR Elect. Coop - McClellan Plant / 

Camden SN-01 48.8 3.301 28.04 444 

Big River Industries / W. Memphis SN-01 30.5 1.524 21.88 330 

Delta Natural Kraft / Pine Bluff SN-02 50.3 2.134 13.29 348 

Domtar, Inc. / Ashdown SN-03 66.1 1.890 26.76 522 

Domtar, Inc. / Ashdown SN-05 71.6 3.659 11.92 325 

Eastman Chemical / Batesville 6M01-01 61.0 2.743 9.45 422 

El Dorado Chemical / El Dorado SN-08 22.9 1.219 33.53 505 

El Dorado Chemical / El Dorado SN-09 22.9 1.219 32.00 500 

El Dorado Chemical / El Dorado SN-10 23.8 0.152 23.77 313 

Entergy - Lake Catherine / Jones Mill SN-03 59.4 5.182 3.08 396 

Entergy - Ritchie Plant / Helena SN-02 71.9 3.658 28.62 390 

Entergy - White Bluff / Redfield SN-01 304.8 7.833 27.43 434 

Entergy - White Bluff / Redfield SN-02 304.8 7.833 27.43 434 

Entergy - White Bluff / Redfield SN-05 4.6 0.914 19.81 519 

Georgia-Pacific Paper / Crossett SN-22 53.3 3.658 10.45 341 

Great Lakes Chemical / El Dorado SN-302A 9.1 0.762 40.54 555 

Green Bay Packaging / Morrilton SN-05A 30.8 1.798 25.60 456 

Lion Oil / El Dorado SN-809 61.0 1.753 9.75 533 

Potlatch Corp. / McGehee SN-04 89.6 2.743 14.78 444 

ASHDOWN-Q8-000647
C-2 

Table C-2 BART-Eligible Emission Units’ Base Elevation and Lambert Conformal 

Conical (LCC) Coordinates 

 

STATIONARY SOURCE 

NAME/LOCATION (BART File Name) 

Emission 

UNIT ID 

Base 

Elevation, 

meters 

(m) 

X Easting 

LCC x 

Y Northing 

LCC y 

Albermarle-South Plant / Magnolia SR-01 86.9 352.81836 -747.03381

Albermarle-South Plant / Magnolia BH-01 88.4 352.67618 -746.98114

Albermarle-South Plant / Magnolia BH-02 88.4 352.65801 -746.98190

American Elect. Power (SWEPCO) / 

Gentry SN-01 349.9 221.58128 -410.39077

Ark. Elect. Coop - Bailey Plant / Augusta SN-01 61.3 510.86643 -507.71488

Ark. Elect. Coop - McClellan Plant / 

Camden SN-01 33.5 390.21870 -702.15534

Big River Industries (General Shale)/ W. 

Memphis SN-01 60.0 609.12652 -517.70639

Delta Natural Kraft / Pine Bluff SN-02 66.4 457.00824 -621.20692

Domtar, Inc. / Ashdown SN-03 97.5 267.47491 -698.66686

Domtar, Inc. / Ashdown SN-05 97.5 267.48245 -698.74355

Eastman Chemical / Batesville 6M01-01 82.3 493.14724 -458.02938

El Dorado Chemical / El Dorado SN-08 63.1 401.11728 -734.65321

El Dorado Chemical / El Dorado SN-09 63.1 401.13533 -734.65236

El Dorado Chemical / El Dorado SN-10 62.2 401.19594 -734.67412

Entergy - Lake Catherine / Jones Mill SN-03 100.0 375.45658 -606.40861

Entergy - Ritchie Plant / Helena SN-02 54.9 586.25363 -591.07129

Entergy - White Bluff / Redfield SN-01 94.2 446.73457 -625.11197

Entergy - White Bluff / Redfield SN-02 94.2 445.61252 -604.15523

Entergy - White Bluff / Redfield SN-05 94.2 445.61539 -604.25671

Georgia-Pacific Paper / Crossett SN-22 46.0 469.03486 -745.02133

Green Bay Packaging / Morrilton SN-05A 98.5 387.29077 -532.44265

Lion Oil / El Dorado SN-809 75.6 403.01817 -741.82948

Potlatch Corp. / McGehee SN-04 43.9 533.13136 -678.59798

ASHDOWN-Q8-000648
D-1 

Appendix D. BART-Eligible Emission Rates used for the NoObs Modeling Run 

Table D-1 BART-eligible units’ highest 24-hour actual emission rates for SO2, NOx, 

PM10*

 and PM2.5 in grams per second (g/sec) 

Highest 24-Hour Actual Emission 

Rates (g/sec) 

BART-Eligible Facilities/ Locations 

Emission Unit 

ID Number SO2 NOx PM10 PM2.5

Albermarle-South Plant / Magnolia SR-01 48.126 0.076 0.000 0.009 

Albermarle-South Plant / Magnolia BH-01 0.353 2.075 0.000 0.136 

Albermarle-South Plant / Magnolia BH-02 0.535 2.578 0.000 0.128 

American Elect. Power (SWEPCO) / 

Gentry SN-01 595.781 245.066 21.725 5.531 

AR Elect. Coop - Bailey Plant / Augusta SN-01 299.344 36.933 21.729 21.729 

AR Elect. Coop - McClellan Plant / 

Camden SN-01 346.189 47.124 28.764 28.764 

Big River Industries/ W. Memphis SN-01 0.000 8.589 0.000 7.076 

Delta Natural Kraft / Pine Bluff SN-02 0.239 1.701 1.058 0.529 

Domtar, Inc. / Ashdown SN-03 0.774 22.632 0.000 21.354 

Domtar, Inc. / Ashdown SN-05 70.175 52.008 0.000 7.881 

Eastman Chemical / Batesville 6M01-01 54.046 11.045 0.290 0.217 

El Dorado Chemical / El Dorado SN-08 0.000 20.060 0.000 0.000 

El Dorado Chemical / El Dorado SN-09 0.000 15.645 0.000 0.000 

El Dorado Chemical / El Dorado SN-10 0.000 0.415 0.000 0.000 

Entergy - Lake Catherine / Jones Mill SN-03 0.420 309.535 0.365 0.246 

Entergy - Ritchie Plant / Helena SN-02 0.105 17.640 0.997 0.997 

Entergy - White Bluff / Redfield SN-01 978.164 550.821 15.592 11.802 

Entergy - White Bluff / Redfield SN-02 985.933 596.075 16.653 12.915 

Entergy - White Bluff / Redfield SN-05 4.095 3.811 0.365 0.246 

Georgia-Pacific Paper / Crossett SN-22 77.275 182.677 0.000 9.310 

Green Bay Packaging / Morrilton SN-05A 4.934 8.771 0.000 1.165 

Lion Oil / El Dorado SN-809 23.142 5.980 0.000 7.696 

Potlatch Corp. / McGehee SN-04 6.942 10.533` 2.752 2.752 

ASHDOWN-Q8-000649
E-1 

Appendix E. Chapter 5 of the CENRAP BART Modeling 

Guidelines (Tesche, et al, 2005) 

5.0 DATA BASES FOR CALPUFF MODELING 

To support BART modeling by the states and source operators, both 

meteorological and aerometric data sets are required. Regional meteorological data sets 

generated by the CALMET model suitable for direct input to the CALPUFF modeling 

system have been developed and archived. These data sets cover calendar years 2001, 

2002, and 2003 for three sub-regional grid domains shown in Figures 5-1 through 5-4. 

The procedures used in developing the CALMET data sets generally follow the IWAQM 

recommendations (EPA, 1998), except for a few notable refinements. The processed 

CALMET files, in CALPUFF-ready input format, are available from CENRAP on hard 

disk drives to interested states and stakeholders. 

This chapter describes how these meteorological modeling sets were developed 

and evaluated. The basic CALMET model configuration used to generate the three years 

of CALPUFF-ready meteorology is described in detail so that users of this information 

have a clear understanding of the data sets and their applicability. In addition, for those 

states or source operators who elect to conduct more source-specific 

CALMET/CALPUFF modeling, the information in this chapter may be helpful in guiding 

specification of revised CALMET model inputs and generation of revised CALMET data 

sets. 

Also included in Section 5 .2 is a discussion of routinely available air quality 

monitoring data sets available to the states and source operators in support of screening 

and source-specific BART modeling exercises. 

5.1 Development of CALMET Meteorological Files 

5.1.1 MM5 Data Sets 

 Alpine Geophysics developed a consistent set of CALMET regional 

meteorological modeling data sets for use by the CENRAP States, BART eligible sources 

within the region and others. These meteorological modeling data sets were constructed 

through the joint use of the CALMET processor and results from existing annual threedimensional MM5 meteorological simulations. The specific annual prognostic model 

simulations available for CENRAP BART modeling included: 

> 2001 MM5 data set at 36/12 km resolution developed for EPA by Alpine 

Geophysics (McNally and Tesche, 2002; McNally 2003); 

> 2002 MM5 data set at 36 km resolution developed for CENRAP by 

Iowa DNR (Johnson, 2003a,b), 

ASHDOWN-Q8-000650
E-2 

> 2003 MM5 data set at 36 km resolution developed for the Midwest RPO 

(Baker, 2005; Baker et al., 2004; Kembell-Cook et al., 2005) 

Each of these studies included a performance evaluation of the MM5 generated data sets 

against surface meteorological observations and the results of these evaluations are 

contained in the reports or presentations cited above. While there exists a set of annual 

12 km MM5 meteorology for 2002, this data set was developed by four independent 

CENRAP modeling centers and these data sets have not been concatenated into one 

master data base. More importantly, there has been no systematic, rigorous model 

performance evaluation performed on the CENRAP 2002 12 km MM5 data yet. 

Accordingly, until such time as the 2002 12 km data set has been evaluated and shown to 

be of comparable reliability as the aforementioned MM5 data sets, it’s use is 

contraindicated. 

5.1.2 CALMET Model Configuration 

 

The CALMET modeling procedures used to construct meteorological inputs to 

CALPUFF for visibility screening of BART eligible sources generally follows the 

IWAQM recommendations (EPA, 1998), except as noted below. 

CALMET Model Options. The CALMET model has a number of user-selected options, 

parameter settings, and ‘switches’ that must be defined prior to exercising the processing 

system. These options and settings are well-described in the CALMET User’s Guide 

(Scire et al., 2000a) and in the CALMET input file to the executable code. Appendix A 

of this protocol summarizes the CALMET configurations used in developing the 

processed 6 km meteorological fields over the three CENRAP BART modeling domains. 

Also included in the tables in Appendix A are the default CALMET options and 

parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998). 

 

CALMET Domain. Three slightly overlapping modeling domains were defined by 

CENRAP to support BART modeling. These domains are shown in Figures 5-1 through 

5-4 and Table 5-1. The processors used to generate the domain, land use, and elevation 

data for the CALMET/CALPUFF system include TERREL, CTGPROC, and 

MAKEGEO, as described below. 

ASHDOWN-Q8-000651
E-3 

> TERREL is the terrain pre-processor that averages terrain features to the 

modeling grid resolution; TERREL constructs the basic properties of the 

gridded domain and defines the coordinates upon which meteorological 

data are stored. Key parameters include specification of grid type, 

location, resolution and terrain elevation. 

> CTGPROC computes the fractional land use for the modeling grid 

resolution. Land use characteristics for each grid cell are assigned using 

CTGPROC. The primary variable adjustment associated with CTGRPOC 

is selection of an appropriate land use database. Version 2.0 of the North 

American Land Cover Characteristics database is used. 

> MAKEGEO is the final pre-processor that combines the terrain and land 

use data for input to CALMET. Generating the appropriate 

MAKEGEO.INP control file requires only minimal alteration of the 

default assignments. Key modifications include specifying domain 

attributes and ensuring input files are correctly referenced. 

Terrain. CALMET requires both terrain height and land use/land cover for the 

application region. These are generated using the CALMET CTGPROC, TERREL and 

MAKEGEO processors. The terrain data were created using the TERREL (version 3.311, 

level 030709) processor and the Shuttle Radar Topography Mission (SRTM)-GTOPO 30 

second (~1 km) resolution dataset. 

Land Use. The landuse data set was created using the Composite Theme Grid CTGROC 

processor (version 2.42, level 030709) and the United States Geological Survey (USGS) 

Global Land Cover Characterization (GLCC) version 2.0 database. The GLCC database 

is available at 30 second (~1km) resolution. References for these and other modeling 

datasets can be found at www.src.com. 

Vertical Layer Structure. The vertical layer structure for the CALMET/CALPUFF 

screening applications is more refined than the general suggestions of IWAQM. The 

CENRAP vertical structure was designed to reduce the need for vertical interpolation 

while simultaneously improving vertical resolution within the planetary boundary layer 

(PBL). Table 5-2 identifies the 11 layer interfaces required to define the 10 layer vertical 

CALMET grid structure. The top interface in the CALMET simulation is 4000 meters. 

Use of Observations. Based on considerable discussions with State and Federal 

managers and agency personnel, CENRAP has elected to use the No-Obs mode in 

CALMET for constructing the 6 km meteorological fields for CALPUFF screening 

exercises. The three annual MM5 simulations (2001, 2002, and 2003) will be used as the 

sole source for meteorological data within CALMET. Blending observational data with 

the MM5 data within CALMET (i.e., use of the “OBS” option is essentially a redundant 

use of the same data. Substantial improvement in the MM5 initialization data and in the 

use of four dimensional data assimilation (FDDA) has been achieved in recent years 

using observational data. The ETA analysis data used in initial and boundary conditions 

estimates as well as within the FDDA fields derive from 3-hourly, 40 km objective 

ASHDOWN-Q8-000652
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analysis fields computed using an extensive supply of observational data (National 

Weather Service surface and upper air data, GOES satellite precipitable water; VAD 

wind profiles from NEXRAD; ACARS aircraft temperature data; SSM/I oceanic surface 

winds; daily NESDIS snow cover and sea-ice analysis data; RAOB balloon drift; GOES 

and TOVS-1B radiance data; 2D-VAR SST from NCEP Ocean Modeling Branch; radar 

estimated rainfall; and surface rainfall). The complexity, resolution, and accuracy of the 

ETA data that is used to initialize and ‘nudge’ the MM5 forecasts is extensive indeed. 

Particularly at the 12-36 km horizontal grid scales over the flat to modestly rolling 

topography of the CENRAP domain, there is no need to introduce local meteorological 

observations in order to retrieve local terrain effects, for example. Thus, mesoscale wind 

patterns are likely to be adequately characterized by the MM5 simulations. 

Many observations, especially surface observations, reflect local conditions on a 

scale smaller than the 6 km CENRAP CALMET fields. The introduction of the local 

observations into the regional modeling domain may extend the influence of the 

observational data beyond its true representativeness and result in internally inconsistent 

flow features. In particular the time interpolation of the 12-hourly upper air sounding 

data may wash out structure in the MM5 fields that are appropriate to retain. Given that 

the CENRAP domain as a whole includes areas of moderately rolling terrain, coastal 

regions and relatively flat terrain, a single set of representative weights1

 that allows 

significant influence of the observations where appropriate, will involve a considerable 

effort and substantial testing. The internally consistent MM5 fields are considered likely 

to be appropriate for the regional simulations, and the incremental benefit of adding the 

observational data into the regional CALMET simulations is not considered worthwhile. 

However, on the smaller domains likely to be considered in source-specific 

modeling (e.g., 1-4 km in scale) with the higher CALMET grid resolution and the smaller 

domain size, more control over the region of influence of the meteorological observations 

can be achieved. It is easier for the diagnostic model to allow the local flow observations 

to have appropriate influence in the vicinity of the observation, but allow terrain-adjusted 

flow to dominate away from the observations. Given that the fine scale source-specific 

domains will be used especially in irregular and/or meteorologically complex settings, the 

relatively coarser-scale MM5 simulations are less likely to be fully adequate, and the 

introduction observational data into CALMET is more likely to achieve improvements in 

the resulting meteorological fields. 

Diagnostic Model Settings

 A number of diagnostic model settings must be selected for CALMET to properly 

process representative diagnostic meteorological data sets. These are summarized in 

Appendix A, compared to the default CALMET settings, and discussed in the following: 

 

1 Weights are assigned in CALMET to control the ‘blending’ of observations and MM5 predictions. 

ASHDOWN-Q8-000653
E-5 

> CALMET options dealing with radius of influence parameters (R1, R2, 

RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in 

No-Observations mode; 

> Gridded cloud data were inferred from the MM5 relative humidity fields 

(ICLOUD=3); 

> Given that all state variables are MM5-derived (IPROG=14; ITPROG=2), 

surface layer winds were not extrapolated to the upper layers (IEXTRP = -

1); 

 > The IWAQM recommendation for disabling the computation of kinematic 

effects in the wind field options and parameters was selected. This was 

selected in light of the very modest elevated terrain in the CENRAP 

domain, relative to the mountainous regions in the U.S. and Alps where 

the kinematic parameterizations were originally developed. Thus, the 

option for computing kinematic effects was disabled (IKINE = 0). 

 > The BIAS array was set to 0. in the CALMET control file because surface 

and upper air data were not used (NOOBS = 2); 

 > Because the MM5 wind fields supply CALMET with the initial guess 

fields to the diagnostic wind model (IWFCOD =1, IPROG = 14) and 

observational data are not reintroduced, the following variables were set to 

nominal values: 

 The minimum distance for which extrapolation of surface winds 

should occur was set to -1 (RMIN2 = -1.). 

 RMIN was left at the IWAQM recommendation of 0.1 km. 

 RMAX1 and RMAX2 were each assigned a value of 30 km. 

RMAX3 was assigned a value of 50 km. 

 R1 and R2 were each assigned the value of 1.0. 

 ISURFT and IUPT were assigned placeholder values of 4 and 2, 

respectively. 

 > The radius of influence regarding terrain features is comparable to the 

resolution of the processed terrain data: 12 km. 

 > The radius of influence for temperature interpolation is set to 36 km 

(TRADKM), a value considered appropriate given the 6 km CALMET 

domain and 36/12 km MM5 domain. 

 > The beginning/ending land use categories for temperature interpolation 

over water are assigned category 55: (JWAT1 = JWAT2 = 55). 

ASHDOWN-Q8-000654
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 > SIGMAP was set to 50 km, while the IWAQM recommendation is 100 

km, but with no supporting documentation. Because precipitation rates 

are explicitly incorporated from the MM5 data, a lower radius of influence 

was deemed appropriate. 

> Diagnostic options: IWAQM default values were used (see Appendix A); 

> TERRAD (terrain scale) is required for runs with diagnostic terrain 

adjustments (i.e., the 2003 simulations). Values of ~10-20 km were 

tested, and an appropriate value determined.

> Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of 

water). This feature allows the temperature field over large bodies of 

water such as the Gulf of Mexico and the Great lakes to be properly 

characterized by buoy observations.

> Mixing height averaging parameter (MNMDAV) were determined 

sensitivity tests. The purpose of the testing is to optimize the variable to 

allow spatial variability in the mixing height field, but without excessive 

noise.

Obviously, there are some instances where more advanced and/or recently developed 

procedures for constructing the CALMET fields have been used compared with the 

IWAQM (1998) guidance. For example, one agency expressed concern about the choice 

to employ prognostic model-derived gridded cloud cover data in CALMET (ICLOUD = 

3). While this is admittedly a ‘non-guideline’ option, in our view it represents the best 

science option currently available. In particular, the EPA CAIR and CAMR rulemaking 

modeling and the CAMx/CMAQ modeling being performed by the RPOs for regional 

haze all utilize the gridded moisture fields in the MM5 model as a basis for estimating 

cloud. Presumably, if the method is suitable for such advanced visibility modeling, it is 

adequate for CALPUFF modeling. Of course, in the protocol negotiation, the States, 

source operators, and regulatory agencies have an opportunity to re-examine the 

CALMET diagnostic model settings used in creating the CENRAP gridded fields and 

modify them if warranted. 

In summary, the development of the regional CALMET meteorological fields 

from MM5 data was conducted in No-Observations (“No-Obs”) mode. CALMET’s 

boundary layer modules were used to compute mixing heights, turbulence coefficients 

and other meteorological parameters required as input to CALPUFF. 

 

 5.1.3 MM5/CALMET Processing 

Construction of the CALPUFF-ready meteorological fields entails a two-step 

process. First, the MM5 prognostic model output fields are extracted and processed for 

input to CALMET. This step entails running various extraction software routines 

ASHDOWN-Q8-000655
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followed by the CALMM5 code. Then, CALMET is exercised for the full three year 

period over each sub-regional CENRAP domain. 

CALMM5. Previous applications of the prognostic Mesoscale Meteorological model 

version 5 (MM5) served as the source of the gridded meteorological fields for calendar 

years 2001, 2002, and 2003. The actual CALMM5 configuration entailed modification 

of a few user-specified variables. However, two setting are of primary importance: 

> All vertical layers from MM5 were extracted, providing CALMET 

configuration flexibility, and 

> Vertical velocity, relative humidity, cloud/rain fields, and ice/snow fields 

were extracted. (Graupel was extracted for 2001, the only year where the 

data were available in the MM5 datasets.) 

CALMET. CALMET (v5.53a, lev 040716) was applied consistent with CENRAP’s 

recommendation that the 6 km be generated using the ‘No-Obs’ option. The specific 

options used have been discussed above and are summarized in Appendix A. 

 

 5.1.4 Evaluation of the CALPUFF-Ready Meteorological Data Sets 

In typical applications the adequacy of the CALMET fields is seldom evaluated 

using independent measurements. Often, only cursory visual examination of wind vector 

plots or time series is considered. This evaluation is important because the CALMET 

performance analysis gives direct insight into the adequacy of the model-processed fields 

on a subregional basis. It also serves as an independent quality assurance tool. Alpine’s 

MAPS evaluation software to perform an independent evaluation of the processed 

CALMET data bases. MAPS was used in conjunction with the NCAR DS472 TDL data 

sets to evaluate the surface winds and temperatures for 2001-2003 across all three 

domains. Since only a small portion of the meteorological content of these data were 

ingested in the MM5 data assimilation routines (see Johnson, 2003a), these data sets are 

essentially an independent, quantitative means for evaluating the adequacy of the 

meteorological fields input to CALPUFF. 

CALMET Evaluation Methodology 

Several statistical measures were calculated as part of the CALMET 

meteorological evaluation using established procedures (e.g., Tesche et al., 1990; 

Emery et al., 2001). Additional plots and graphs are used to present these statistics on 

both hourly and daily time frames over the full annual cycle. For this study, evaluation 

measures were calculated for wind, temperature, and relative humidity because these 

parameters are the principal meteorological inputs to CALPUFF. The full set of 

CALMET evaluation statistics and graphical displays generated with the AG-MAPS 

ASHDOWN-Q8-000656
E-8 

software (McNally and Tesche,1994) are contained on a DVD available from 

CENRAP. 

The statistics used to evaluate the meteorological fields for 2001-2003 are 

generated in both absolute terms (e.g., wind speed error in m/s), and relative terms 

(percent error) as is commonly done for air quality assessments. Obviously, a very 

different significance is associated with a given relative error for different 

meteorological parameters. For example, a 10% error for wind speed measured at 10 

m/s is an absolute error of 1 m/s, a minor error. Yet a 10% error for temperature at 

300 K is an absolute error of 30 K, a ridiculously large error. On the other hand, 

pollutant concentration errors of 10% at 1 ppb or 10 ppm carry practically the same 

significance. 

Three key meteorological metrics include the bias, error, and index of 

agreement (IOA) for wind speed, temperature and relative humidity. These measures 

are defined as follows: 

Bias (B): Calculated as the mean difference in prediction-observation pairings with 

valid data within a given analysis region and for a given time period (hourly or daily): 

Error (E): Calculated as the mean absolute difference in prediction-observation 

pairings with valid data within a given analysis region and for a given time period 

(hourly or daily). 

Note that the bias and gross error for winds are calculated from the predicted-observed 

residuals in speed and direction (not from vector components u and v). The direction 

error for a given prediction-observation pairing is limited to range from 0 to ±180°. 

Index of Agreement (IOA): calculated following the approach of Willmont (1981). 

This metric condenses all the differences between model estimates and observations 

within a given analysis region and for a given time period (hourly and daily) into one 

statistical quantity. It is the ratio of the total RMSE to the sum of two differences – 

between each prediction and the observed mean, and each observation and the observed 

mean: 

∑∑( ) = =

= −

J

j

I

i

i

j

i

j OP

IJ

B

1 1

1

∑∑= =

= −

J

j

I

i

i

j

i

j OP

IJ

E

1 1

1

























−+−

⋅ −=

∑∑= =

J

j

I

i

o

i

jo

i

j MOMP

RMSEIJ IOA

1 1

2

1

ASHDOWN-Q8-000657
E-9 

Viewed from another perspective, the index of agreement is a measure of the match 

between the departure of each prediction from the observed mean and the departure of 

each observation from the observed mean. Thus, the correspondence between 

predicted and observed values across the domain at a given time may be quantified in a 

single metric and displayed as a time series. The index of agreement has a theoretical 

range of 0 to 1, the latter score suggesting perfect agreement. 

CALMET Evaluation Results 

Table 5-5 summarizes the statistical measures, averaged over the month, for 

temperature, wind speed, and relative humidity for all three years. The CALMET 

evaluation DVD contains a full compilation of the statistical and graphical results. 

Figures 5-7 through 5-31 present a variety of graphical displays of processed and 

observed surface temperature, relative humidity, and wind across the three CENRAP 

subdomains for the three-year period 2001-2003. Figures 5-28 through 5-31 provide 

convenient summaries of the bias and error in the relative humidity, temperature, and 

wind speed fields across the continuous 36 month period by subdomain. 

Thorough discussion of the performance findings is beyond the scope of these 

guidelines. However, a few key findings of the evaluation are worth noting here. From 

Table 5-5, the wind speed index of agreement, a general measure of correlation between 

measured and observed winds, is systematically greater than a value of 0.8 for virtually 

every month. These values are typically better than those generally achieved in urbanand regional-scale model applications for ozone SIPs. For example, the statistical 

benchmark for IOA suggested by Emery et al., (2001) is IOA > 0.6. Thus, the wind 

speed agreement for all three domains and all three years appears quite good relative to 

other MM5/RAMS model applications. From Figure 5-11, the wind speed root mean 

square error for the Central domain for 2002 is generally below 2.0 m/s, the performance 

goal for this parameter. From Figure 5-29 (as well as in Table 5-5), the temperature bias 

results for the 36 month are generally quite close to the + 0.5 deg C performance goal. As 

shown in Figure 5-30 the temperature error results are slightly poorer than the 2 deg C 

performance goal for 2001 and 2003, but are below the 2.0 deg C threshold for 2002. 

Note that the benchmarks were developed not to provide a pass/fail standard to which all 

modeling results should be held, but rather to put the results into an historical context. 

In summary, we find that: 

 Relative Humidity 

 Bias over three-year period near zero all domains 

 For some months over- and under-prediction (up to 10% or more) is 

evident – no discernable trend 

 Errors typically diminish from 2001 through 2003, and are generally < 

12% after 1st quarter of 2001. 

 Surface Temperatures 

ASHDOWN-Q8-000658
E-10 

 Monthly averaged temperatures are systematically biased low (cooler) by 

0.25 to 1.25 deg C. 

 The errors in monthly averaged temperatures typically range between 1.8 

and 2.6 deg C 

 Average error over all months is about 2.2 deg C. 

 Surface Wind Speeds 

 IOA typically between 0.8 0-0.9 

 Seasonally variable 

 Central subdomain gives best correlation 

 Results from MM5/CALMET evaluation provide potentially useful information 

for diagnosing BART visibility modeling analyses 

 MM5/CALMET fields exhibit good statistical agreement with observations, in 

part because observations figure prominently in the construction of the 

interpolated CALMET fields. 

 MM5/CALMET fields for the three CENRAP subdomains are quite sufficient for 

use in CALPUFF modeling. 

 5.1.5 Meteorological Data Archive and Distribution 

All models, scripts and CALMET data (excepting MM5 outputs) are available 

from CENRAP on appropriate external combination Firewire/USB drives. 

5.2 Aerometric Monitoring Networks 

Data from ambient monitoring networks for both gas-phase and aerosol species 

are available for use in CENRAP BART modeling analyses. Table 5-4 summarizes 

ambient monitoring networks. Data for 2002 have been compiled for all networks 

covering the CENRAP domain with the exception of the PAMS and PM Supersites. 

These data sets may be obtained from CENRAP. Figures 5-5 and 5-6 display the 

locations of monitoring sites in and near the CENRAP States. 

ASHDOWN-Q8-000659
E-11 

Table 5-1. CENRAP Lambert Conic Conformal Modeling Domain Specifications 

(40.97 degree projection origin; 33 and 45 degree matching parallels). 

Domain Southwest 

Coordinate (km)

Number 

of X 

grid cells

Number 

of Y 

grid cells 

Horizontal

Resolution 

CALMET 

 South -1008, -1620 306 246 6 km 

 Central -1008, -864 388 234 6 km 

 North -1008, 0 300 193 6 km 

Table 5-2. Vertical Layer Structure in CALMET Fields. (Heights are in meters.) 

LAYER 

NUMBER

LAYER 

HEIGHT

LAYER 

NUMBER

LAYER 

HEIGHT

0 0. 6 640. 

1 20. 7 1200. 

2 40. 8 2000. 

3 80. 9 3000. 

4 160. 10 4000. 

5 320. 

Table 5-3. Meteorological Model File Sizes for CENRAP BART Modeling. 

Domain Monthly Annual 3 Years Domain Grid 3 years

North 4.6 55.2 165.6 2001 12 km 1370

Central 6.6 79.2 237.6 2002 36 km 430

South 6.0 72.0 216.0 2003 36 km 430

total 17.2 206.4 619.2 total 2230

CALMET 6 km File Sizes, (Gbytes) MM5 File Sizes, (Gbytes)

ASHDOWN-Q8-000660
E-12 

Table 5-4. Statistical Evaluation of the CALMET Meteorological Fields for 2001-

2003. 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 4.54 3.19 0.17 -14.55 -12.09 -4.35 -0.62 1.17 -2.07 -7.98 -6.62 -4.22 -3.62

 Central -2.60 -7.28 -11.38 -10.69 -8.62 -2.90 0.66 1.07 -1.44 -5.46 -6.16 -7.78 -5.21

 South -10.23 -11.53 -13.78 -4.24 -2.08 0.99 4.12 3.16 -0.12 -2.12 -3.44 -9.76 -4.09

RH Error (%)

 North 10.06 10.31 14.03 18.77 16.28 12.39 11.82 11.76 13.26 15.54 13.53 12.89 13.39

 Central 13.32 15.86 17.45 17.05 14.50 11.67 11.52 11.32 12.26 15.52 14.79 14.95 14.18

 South 16.22 18.37 18.17 13.26 12.15 11.51 12.09 12.40 11.82 14.85 14.73 16.19 14.31

Temp Bias (0

C)

 North -1.63 -1.23 -1.23 -0.24 0.08 -0.29 -0.23 -0.54 -0.55 -0.09 -0.40 -1.27 -0.64

 Central -0.99 -0.65 -0.54 -0.16 0.13 -0.23 -0.43 -0.54 -0.36 -0.34 -0.30 -0.74 -0.43

 South -0.47 -0.42 0.03 -0.31 -0.33 -0.63 -0.99 -0.85 -0.52 -0.36 -0.19 -0.21 -0.44

Temp Error (0

C)

 North 3.10 2.88 2.54 2.49 2.44 2.43 2.42 2.49 2.58 2.48 2.89 2.55 2.61

 Central 2.38 2.25 1.99 2.18 1.99 2.01 2.07 2.11 2.21 2.52 2.61 2.42 2.23

 South 2.31 2.28 1.92 2.13 2.01 2.17 2.19 2.21 2.19 2.70 2.49 2.50 2.26

Wind Speed IOA

 North 0.79 0.83 0.83 0.87 0.86 0.85 0.81 0.84 0.84 0.82 0.81 0.79 0.83

 Central 0.85 0.87 0.88 0.88 0.89 0.86 0.84 0.86 0.87 0.86 0.85 0.84 0.86

 South 0.81 0.80 0.85 0.79 0.83 0.83 0.78 0.80 0.82 0.82 0.80 0.82 0.81

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 8.33 9.52 6.63 0.95 -2.42 1.25 2.43 1.60 0.57 0.47 4.47 7.73 3.46

 Central 7.43 5.13 4.60 1.65 -1.02 1.52 2.50 1.88 -0.27 -1.40 -0.01 4.35 2.20

 South 3.08 -1.19 2.53 2.32 1.26 1.98 2.51 2.62 -0.80 -2.42 -4.45 -1.03 0.53

RH Error (%)

 North 11.85 13.18 11.61 11.13 11.90 10.04 9.54 9.08 10.26 10.26 11.55 11.61 11.00

 Central 12.21 12.43 11.26 10.58 10.72 9.89 9.55 9.54 10.22 10.25 11.42 11.26 10.78

 South 11.24 11.76 10.34 8.95 9.30 9.49 9.46 9.61 9.68 9.33 11.63 10.95 10.14

Temp Bias (0

C)

 North -0.70 -0.82 -0.96 -0.52 -0.25 -0.36 -0.53 -0.49 -0.44 -0.67 -0.76 -0.69 -0.60

 Central -0.57 -0.65 -0.79 -0.62 -0.41 -0.68 -0.81 -0.74 -0.49 -0.54 -0.55 -0.52 -0.61

 South -0.23 -0.13 -0.52 -0.61 -0.61 -0.94 -0.94 -1.07 -0.65 -0.47 0.04 -0.13 -0.52

Temp Error (0

C)

 North 2.15 2.07 2.04 1.89 1.86 1.83 1.86 1.80 1.95 1.78 1.99 2.15 1.95

 Central 2.12 2.05 2.14 1.95 1.91 1.93 1.93 1.92 2.02 1.77 2.00 2.00 1.98

 South 2.18 2.05 2.17 1.83 1.89 1.91 1.88 2.00 1.92 1.68 2.06 1.93 1.96

Wind Speed IOA

 North 0.82 0.84 0.86 0.88 0.86 0.85 0.85 0.83 0.85 0.85 0.81 0.78 0.84

 Central 0.87 0.88 0.90 0.90 0.88 0.87 0.84 0.84 0.87 0.88 0.85 0.85 0.87

 South 0.86 0.86 0.85 0.85 0.84 0.82 0.79 0.80 0.83 0.83 0.83 0.82 0.83

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 10.15 7.40 6.01 0.93 -3.76 -0.38 1.38 2.04 -1.66 -1.99 2.96 7.68 2.56

 Central 6.94 4.76 4.15 0.42 -2.18 0.17 2.08 2.13 -2.05 -4.13 0.00 5.47 1.48

 South 0.00 0.00 0.47 -1.10 -0.37 0.54 1.77 2.89 -3.31 -6.01 -3.66 -0.33 -0.76

RH Error (%)

 North 13.30 11.21 12.32 11.70 11.65 10.03 9.70 9.57 11.13 12.68 11.53 11.85 11.39

 Central 12.77 10.95 11.61 11.18 10.33 9.91 9.49 9.50 10.70 12.69 12.10 12.43 11.14

 South 11.18 10.00 9.85 10.17 9.20 9.54 8.90 9.91 10.21 12.12 12.15 12.39 10.47

Temp Bias (0

C)

 North -1.24 -0.99 -0.63 -0.29 -0.11 -0.10 -0.22 -0.49 -0.34 0.29 -0.85 -1.34 -0.53

 Central -0.84 -0.80 -0.64 -0.47 -0.27 -0.36 -0.60 -0.66 -0.32 0.30 -0.54 -0.89 -0.51

 South -0.17 -0.27 -0.36 -0.43 -0.46 -0.62 -0.91 -0.98 -0.28 0.53 0.00 -0.03 -0.33

Temp Error (0

C)

 North 2.31 2.15 2.14 2.02 1.81 1.77 1.91 1.98 2.25 2.57 2.30 2.67 2.16

 Central 2.14 2.03 2.15 2.13 1.80 1.81 1.96 1.99 2.16 2.54 2.31 2.45 2.12

 South 2.10 1.90 2.00 2.08 1.84 1.81 1.88 2.06 1.94 2.40 2.28 2.48 2.06

Wind Speed IOA

 North 0.79 0.81 0.83 0.86 0.87 0.85 0.86 0.87 0.84 0.82 0.80 0.82 0.83

 Central 0.85 0.88 0.87 0.89 0.90 0.87 0.87 0.86 0.87 0.87 0.86 0.86 0.87

 South 0.83 0.83 0.85 0.83 0.85 0.81 0.83 0.82 0.85 0.84 0.82 0.82 0.83

CALMET Model Evaluation Statistics for 2001. 

CALMET Model Evaluation Statistics for 2002. 

CALMET Model Evaluation Statistics for 2003. 

ASHDOWN-Q8-000661
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Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP 

Domain. 

Monitoring Network Chemical Species Measured Sampling Period Data Availability/Source 

The Interagency 

Monitoring of Protected 

Visual Environments 

(IMPROVE) 

Speciated PM25 and PM10 

(see species mappings) 

1 in 3 days; 24 hr 

average 

http://vista.cira.colostate.e

du/improve/Data/IMPRO

VE/improve_data.htm

Clean Air Status and 

Trends Network 

(CASTNET) 

Speciated PM25, Ozone (see 

species mappings) 

Approximately 1-

week average 

http://www.epa.gov/castne

t/data.html

National Atmospheric 

Deposition Program 

(NADP) 

Wet deposition (hydrogen 

(acidity as pH), sulfate, nitrate, 

ammonium, chloride, and base 

cations (such as calcium, 

magnesium, potassium and 

sodium)), Mercury 

1-week average http://nadp.sws.uiuc.edu/

Air Quality System 

(AQS) Aka Aerometric 

Information Retrieval 

System (AIRS) 

CO, NO2, O3, SO2, PM25, 

PM10, Pb 

Typically hourly 

average 

http://www.epa.gov/air/dat

a/

Speciation Trends 

Network (STN) 

Speciated PM 24-hour average http://www.epa.gov/ttn/am

tic/amticpm.html

Southeastern Aerosol 

Research and 

Characterization 

(SEARCH) 

(Southeastern US only) 

24-hr PM25 (FRM Mass, OC, 

BC, SO4, NO3, NH4, Elem.); 

24-hr PM coarse (SO4, NO3, 

NH4, elements); Hourly PM2.5 

(Mass, SO4, NO3, NH4, EC, 

TC); Hourly gases (O3, NO, 

NO2, NOy, HNO3, SO2, CO) 

Hourly or 24-hour 

average, depending 

on parameter. 

Electric Power Research 

Institute (EPRI), Southern 

Company, and other 

companies. 

http://www.atmosphericresearch.com

EPA Particulate Matter 

Supersites 

(Includes St. Louis in the 

CENRAP region) 

Speciated PM25 http://www.epa.gov/ttn/am

tic/supersites.html

Photochemical 

Assessment Monitoring 

Stations (PAMS) 

Varies for each of 4 station 

types. 

http://www.epa.gov/ttn/am

tic/pamsmain.html

National Park Service 

Gaseous Pollutant 

Monitoring Network 

Acid deposition (Dry; SO4, 

NO3, HNO3, NH4, SO2), O3, 

meteorological data 

Hourly http://www2.nature.nps.go

v/ard/gas/netdata1.htm

ASHDOWN-Q8-000662
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Figure 5-1. CENRAP North, Central, and South 6 km Meteorological Domains. 

ASHDOWN-Q8-000663
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Figure 5-2. CENRAP South Domain. 

ASHDOWN-Q8-000664
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Figure 5-3. CENRAP Central Domain. 

ASHDOWN-Q8-000665
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Figure 5-4. CENRAP North Domain. 

ASHDOWN-Q8-000666
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Figure 5-5. Locations of IMPROVE, CASTNet, SEARCH, STN and NADP Monitoring 

Sites in and Near the CENRAP States. 

ASHDOWN-Q8-000667
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Figure 5-6. Locations of AQS Monitoring Sites in and Near the CENRAP States.

ASHDOWN-Q8-000668
E-20 

Figure 5-7. Spatial Mean Relative Humidity (%) over the Central Domain: July 2002.

Figure 5-8. Spatial Mean Surface Temperature (deg C) over the Central Domain: July 

2002. 

ASHDOWN-Q8-000669
E-21 

Figure 5-9. Wind Speed Index of Agreement over the Central Domain: July 2002. 

Figure 5-10. Standard Deviation in Wind Speed (m/s) over the Central Domain: July 

2002. 

ASHDOWN-Q8-000670
E-22 

Figure 5-11. Root Mean Square Error in Wind Speed (m/s) over the Central Domain: July 

2002. 

Figure 5-12. Scalar Mean Wind Speed (m/s) over the Central Domain: July 2002. 

ASHDOWN-Q8-000671
E-23 

Figure 5-13. Vector Mean Wind Speed (m/s) over the Central Domain: July 2002.

Figure 5-14. Normalized Bias in Relative Humidity (%) over the Central Domain: July 

2002. 

ASHDOWN-Q8-000672
E-24 

Figure 5-15. Normalized Error in Relative Humidity (%) over the Central Domain: July 

2002. 

Figure 5-16. Relative Humidity (%) at Kenosha, WI: July 2002. 

ASHDOWN-Q8-000673
E-25 

Figure 5-17. Relative Humidity (%) at Topeka, KS: July 2002. 

Figure 5-18. Normalized Bias in Surface Temperature (%) over the Central Domain: July 

2002. 

ASHDOWN-Q8-000674
E-26 

Figure 5-19. Normalized Error in Surface Temperature over the Central Domain: July 

2002.

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002. 

ASHDOWN-Q8-000675
E-27 

Figure 5-21. Surface Temperature at Topeka, KS: July 2002. 

ASHDOWN-Q8-000676
E-28 

Figure 5-22. MM5/CALMET Relative Humidity Bias (%) by Month for Three BART 

Modeling Years (2001, 2003, and 2003).

ASHDOWN-Q8-000677
E-29 

Figure 5-23. MM5/CALMET Relative Humidity Error (%) by Month for Three BART 

Modeling Years (2001, 2003, and 2003). 

ASHDOWN-Q8-000678
E-30 

Figure 5-24. MM5/CALMET Temperature Bias (deg C) by Month for Three BART 

Modeling Years (2001, 2003, and 2003). 

ASHDOWN-Q8-000679
E-31 

Figure 5-25. MM5/CALMET Temperature Error (deg C) by Month for Three BART 

Modeling Years (2001, 2003, and 2003). 

ASHDOWN-Q8-000680
E-32 

Figure 5-26. MM5/CALMET Wind Speed Index of Agreement by Month for Three 

BART Modeling Years (2001, 2003, and 2003). 

ASHDOWN-Q8-000681
E-33 

Figure 5-27. MM5/CALMET Relative Humidity Bias (%) over Three Years in All 

CENRAP Domains. 

ASHDOWN-Q8-000682
E-34 

Figure 5-28. MM5/CALMET Relative Humidity Error (%) over Three Years in All 

CENRAP Domains. 

ASHDOWN-Q8-000683
E-35 

Figure 5-29. MM5/CALMET Surface Temperature Bias (deg C) over Three Years in All 

CENRAP Domains. 

ASHDOWN-Q8-000684
E-36 

Figure 5-30. MM5/CALMET Surface Temperature Error (deg C) over Three Years in All 

CENRAP Domains. 

ASHDOWN-Q8-000685
E-37 

Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All 

CENRAP Domains. 

ASHDOWN-Q8-000686
F-1 

Appendix F. CALMET Input Control Parameters 

Table F-1 Input Groups in the CALMET Control File.

Input 

Group Description Applicable 

0 Input and output file names Yes 

1 General run control parameters Yes 

2 Map Projection and Grid Control Parameters Yes 

3 Output Options Yes 

4 Meteorological Data Options Yes 

5 Wind field Options and Parameters Yes 

6 Mixing Height, Temperature and Precipitation Parameters Yes 

7 Surface Meteorological Station Parameters Yes 

8 Upper Air Meteorological Station Parameters Yes 

9 Precipitation Station Parameters Yes 

Table F-2 CALMET Model Input Group 0: Input and Output File Names

Parameter Default Value Comments 

Input GEO.DAT GEO.DAT 

Input SURF.DAT SURF.DAT 

Input CLOUD.DAT CLOUD.DAT 

Input PRECIP.DAT PRECIP.DAT 

Input MM4.DAT MM4.DAT 

Input WT.DAT WT.DAT 

Output CALMET.LST CALMET.LST 

Output CALMET.DAT CALMET.DAT 

Output PACOUT.DAT PACOUT.DAT 

NUSTA -- 0 Number of upper air stations 

NOWSTA -- 0 Number of over water stations 

Input UP1.DAT UP1.DAT 

Input UP2.DAT UP2.DAT 

Input UP3.DAT UP3.DAT 

Input SEA1.DAT SEA1.DAT 

Input DIAG.DAT DIAG.DAT 

Input PROG.DAT PROG.DAT 

Output TEST.PRT TEST.PRT 

Output TEST.OUT TEST.OUT 

Output TEST.KIN TEST.KIN 

Output TEST.FRD TEST.FRD 

Output TEST.SLP TEST.SLP 

ASHDOWN-Q8-000687
F-2 

Table F-3 CALMET Model Input Group 1: General Run Control Parameters 

Parameter Default Value Comments 

IBYR - 2001 Starting year 

IBMO - 1 Starting month 

IBDY - 1 Starting day 

IBHR - 1 Starting hour 

IBTZ - 6 Base time zone 

IRLG - 8760 Length of run 

IRTYPE 1 1 Run type (must = 1 to run CALPUFF) 

LCALGRD T F Compute CALGRID data fields 

ITEST 2 2 Stop run after SETUP to do input QA 

Table F-4 CALMET Model Input Group 2: Map Projection and Grid Control Parameters 

Parameter Default Value Comments 

PMAP UTM LCC Map Projection 

RLATO -- 40N Latitude (dec. degrees) of projection origin 

RLONO -- 97W Longitude (dec. degrees) of projection origin 

XLAT1 -- 33N Matching parallel(s) of latitude for projection 

XLAT2 -- 45N Matching parallel(s) of latitude for projection 

DATUM WGS-G WGS-G 

NX -- 300 Number of X grid cells in meteorological grid 

NY -- 192 Number of Y grid cells in meteorological grid 

DGRIDKM -- 6.0 Grid spacing, km 

XORIGKM -- -1008. Ref. Coordinate of SW corner of grid cell (1,1) 

YORIGKM -- 0.0 Ref. Coordinate of SW corner of grid cell (1,1) 

NZ -- 10 No. of vertical layers 

ZFACE -- 0, 20 40, 

80, 160, 

320, 640, 

1200, 2000, 

3000, 4000 

Cell face heights in arbitrary vertical grid, m 

ASHDOWN-Q8-000688
F-3 

Table F-5 CALMET Model Input Group 3: Output Options 

Parameter Default Value Comments 

LSAVE T T Disk output option 

IFORMO 1 1 Type of unformatted output file 

LPRINT F F Print met fields 

IPRINF 1 1 Print intervals 

IUVOUT(NZ) NZ*0 NZ*0 Specify layers of u,v wind components to 

print 

IWOUT(NZ) NZ*0 NZ*0 Specify layers of w wind component to print 

ITOUT(NZ) NZ*0 NZ*0 Specify levels of 3-D temperature field to 

print 

LDB F F Print input met data and variables 

NN1 1 1 First time step for debug data to be printed 

NN2 1 1 Last time step for debug data to be printed 

IOUTD 0 0 Control variable for writing test/debug wind 

fields 

NZPRN2 1 0 Number of levels starting at surface to print 

IPR0 0 0 Print interpolated wind components 

IPR1 0 0 Print terrain adjusted surface wind 

components 

IPR2 0 0 Print initial divergence fields 

IPR3 0 0 Print final wind speed and direction 

IPR4 0 0 Print final divergence fields 

IPR5 0 0 Print winds after kinematic effects 

IPR6 0 0 Print winds after Froude number adjustment 

IPR7 0 0 Print winds after slope flows are added 

IPR8 0 0 Print final wind field components 

Table F-6 CALMET Model Input Group 4: Meteorological Data Options 

Parameter Default Value Comments 

NOOBS 0 2 2 = No surface, over water, or upper air 

observations; use MM5 for surface, over water, and 

upper air data 

NSSTA -- 0 Number of meteorological surface stations 

NPSTA -- 0 Number of precipitation stations 

ICLOUD -- 3 Gridded cloud fields 

IFORMS 2 2 Formatted surface meteorological data file 

IFORMP 2 2 Formatted surface precipitation data file 

IFORMC 2 2 Formatted cloud data file 

ASHDOWN-Q8-000689
F-4 

Table F-7 CALMET Model Input Group 5: Wind field Options and Parameters 

Parameter Default CENRAP Comments 

IWFCOD 1 1 Model selection variable 

IFRADJ 1 1 Compute Froude number adjustment effects? 

IKINE 0 0 Compute kinematic effects? 

IOBR 0 0 Use O’Brien (1970) vertical velocity 

adjustment? 

ISLSOPE 1 1 Compute slope flow effects? 

IEXTRP -4 -1 Extrapolate surface wind obs to upper levels? 

ICALM 0 0 Extrapolate surface winds even if calm? 

BIAS NZ*0 0, 0, 0, 0, 0, 

0, 0, 0, 0, 0 

Layer-dependent biases weighting aloft 

measurements 

RMIN2 4. -1.0 Minimum vertical extrapolation distance 

IPROG 0 14 14 = Yes, use winds from MM5.DAT file as 

initial guess field [IWFCOD = 1 

ISTEPPG 1 1 MM5 output time step 

LVARY F T Use varying radius of influence 

RMAX1 -- 30. Maximum radius of influence over land in sfc 

layer 

RMAX2 -- 30. Maximum radius of influence over land aloft 

RMAX3 -- 50. Maximum radius of influence over water 

RMIN 0.1 0.1 Minimum radius of influence used anywhere 

TERRAD -- 12. Terrain features radius of influence 

R1 -- 1. Weighting of first guess surface field 

R2 -- 1. Weighting of first guess aloft field 

RPROG -- 0. MM5 windfield weighting parameter 

DIVLIM 5.E-6 5.E-6 Minimum divergence criterion 

NITER 50 50 Number of divergence minimization iterations 

NSMMTH 2, 4, 4, 4, 

4, 4, 4 

2, 4, 4, 4, 4, 

4, 4 

Number of passes through smoothing filter in 

each layer of CALMET 

NITR2 99. 5, 5, 5, 5, 5, 

5, 5, 5, 5, 5 

Maximum number of stations used in each 

layer for the interpolation of data to a grid 

point 

CRITFN 1.0 1.0 Critical Froude number 

ALPHA 0.1 0.1 Kinematic effects parameter 

FEXTR2 NZ*0.0 NZ*0.0 Scaling factor for extrapolating sfc winds aloft 

NBAR 0 0 Number of terrain barriers 

IDIOTP1 0 0 Surface temperature computation switch 

ISURFT -- 4 Number of sfc met stations to use for temp 

calcs 

IDIOPT2 0 0 Domain-averaged lapse rate switch 

IUPT 0 2 Upper air stations to use for lapse rate 

calculation 

ZUPT 200. 200. Depth through which lapse rate is calculated 

IDIOPT3 0 0 Domain-averaged wind component switch 

ASHDOWN-Q8-000690
F-5 

IUPWND -1 -1 Number of aloft stations to use for wind calc 

ZUPWND 1., 1000. 1., 

1000. 

Bottom and top of layer through which the 

domain-scale winds are computed 

IDIOPT4 0 0 Observed surface wind component switch 

IDIOPT5 0 0 Observed aloft wind component switch 

LLBREZE F F Use Lake Breeze Module 

NBOX 0 0 Number of lake breeze regions 

NLB -- 0 Number of stations in the region 

METBXID(NLB) -- 0 Station ID’s in the region 

Table F-8 CALMET Model Input Group 6: Mixing Height, Temperature and 

Precipitation

Parameter Default Value Comments 

CONSTB 1.41 1.41 Neutral stability mixing height coefficient 

CONSTE 0.15 0.15 Convective stability mixing height coefficient 

CONSTN 2400. 2400. Stable stability maxing height coefficient 

CONSTW 0.16 0.16 Over water mixing height coefficient 

FCORIOL 1.E-4 1.E-4 Absolute value of Coriolis parameter 

IAVEZI 1 1 Conduct spatial averaging? Yes = 1 

MNMDAV 1 10 Maximum search radius in averaging process 

HAFANG 30. 30. Half-angle of upwind looking cone for averaging 

ILEVZI 1 1 Layers of wind use in upwind averaging 

DPTMIN 0.001 0.001 Minimum potential temperature lapse rate in the 

stable layer above the current convective mixing ht 

DZZI 200. 200. Depth of layer above current conv. mixing height 

through which lapse rate is computed 

ZIMIN 50. 50. Minimum overland mixing height 

ZIMAX 3000. 3000. Maximum overland mixing height 

ZIMINW 50. 50. Minimum over water mixing height 

ZIMAXW 3000. 3000. Maximum over water mixing height 

ITPROG 0 2 3D temperature from observations or from MM5? 

IRAD 1 1 Type of interpolation; 1 = 1/R 

TRADKM 500. 36. Temperature interpolation radius of influence 

NUMTS 5 5 Max number of stations for temp interpolation 

IAVET 1 1 Spatially average temperatures? 1 = yes 

TGDEFB -.0098 -.0098 Temp gradient below mixing height over water 

TGDEFA -.0045 -.0045 Temp gradient above mixing height over water 

JWAT1 -- 55 Beginning land use categories over water 

JWAT2 -- 55 Ending land use categories for water 

NFLAGP 2 2 Precipitation interpolation flag; 2 = 1/R-squared 

SIGMAP 100. 50. Radius of influence for precipitation interpolation 

CUTP 0.01 0.01 Minimum precipitation rate cutoff (mm/hr) 

ASHDOWN-Q8-000691
G-1 

Appendix G. CALPUFF Input Control Parameters 

Table G-1 Input Groups in the CALPUFF Control File

Input 

Group Description Applicable 

0 Input and output file names Yes 

1 General run control parameters Yes 

2 Technical options Yes 

3 Species list Yes 

4 Grid control parameters Yes 

5 Output options Yes 

6 Sub grid scale complex terrain inputs Yes 

7 Dry deposition parameters for gases Yes 

8 Dry deposition parameters for particles Yes 

9 Miscellaneous dry deposition for parameters Yes 

10 Wet deposition parameters Yes 

11 Chemistry parameters Yes 

12 Diffusion and computational parameters Yes 

13 Point source parameters Yes 

14 Area source parameters No 

15 Line source parameters No 

16 Volume source parameters No 

17 Discrete receptor information Yes 

Table G-2 CALPUFF Model Input Group 0: Input and Output File Names 

Parameter Default Value Comments 

METDAT CALMET.DAT Not used Input file name 

PUFLST CALPUFF.LST Varies with facility CALPUFF output file name 

CONDAT CONC.DAT Varies with facility Concentration output file 

name 

DFDAT DFLX.DAT Varies with facility Dry flux output file name 

WFDAT WFLX.DAT Varies with facility Wet flux output file name 

VISDAT VISB.DAT Varies with facility Visibility output file name 

OZDAT OZONE.DAT Varies with year Ozone input file name 

LCFILES - T File names converted to lower 

case 

NMETDAT 1 12 Number of CALMET.DAT 

files for run 

CALMET.DAT - METDAT=/location of 

CALMET.DAT files 

12 entries one for each month 

ASHDOWN-Q8-000692
G-2 

Table G-3 CALPUFF Model Input Group 1: General Run Control Parameters 

Parameter Default Value Comments 

METRUN 0 0 All model periods in met file(s) will be run 

IBYR - See note 

1 below 

Starting year 

IBMO - 1 Starting month 

IBDY - 1 Starting day 

IBHR - 1 Starting hour 

XBTZ - 0 Time zone for met files (0 = GMT) 

IRLG - See note 

2 below 

Length of run 

NSPEC 5 10 Number of MESOPUFF II chemical species 

NSE 3 See note 

3 below 

Number of chemical species to be emitted 

ITEST 2 2 Program is executed after SETUP phase 

MRESTART 0 0 Do not read or write a restart file during run 

NRESPD 0 0 File written only at last period 

METFM 1 1 CALMET binary file (CALMET.MET) 

AVET 60 60 Averaging time in minutes 

PGTIME 60 60 PG Averaging time in minutes 

Note 1: Enter the year being modeled (i.e. 2001, 2002, or 2003) 

Note 2: Enter 8760 for the years 2001 and 2002 but enter 8748 for the year 2003 

Note 3: Enter 6 for the no-obs run and 7 for the refined run 

Table G-4 CALPUFF Model Input Group 2: Technical Options 

Parameter Default Value Comments 

MGAUSS 1 1 Gaussian distribution used in near field 

MCTADJ 3 3 Partial plume path terrain adjustment 

MCTSG 0 0 Sub-grid-scale complex terrain not modeled 

MSLUG 0 0 Near-field puffs not modeled as elongated 

MTRANS 1 1 Transitional plume rise modeled 

MTIP 1 1 Stack tip downwash used 

MSHEAR 0 0 (0, 1) Vertical wind shear (not modeled, 

modeled) 

MSPLIT 0 0 Puffs are not split 

MCHEM 1 1 MESOPUFF II chemical parameterization 

scheme 

MAQCHEM 0 0 Aqueous phase transformation not modeled 

MWET 1 1 Wet removal modeled 

MDRY 1 1 Dry deposition modeled 

MDISP 3 2 AERMOD dispersion coefficients 

MTURBVW 3 3 Use both σv and σw from PROFILE.DAT to 

compute σy and σz (n/a) 

MDISP2 3 2 AERMOD dispersion coefficients 

MROUGH 0 0 PG σy and σz not adjusted for roughness 

MPARTL 1 1 No partial plume penetration of elevated 

inversion 

ASHDOWN-Q8-000693
G-3 

MTINV 0 0 Strength of temperature inversion computed 

from default gradients 

MPDF 0 0 PDF not used for dispersion under 

convective 

conditions 

MSGTIBL 0 0 Sub-grid TIBL module not used for shoreline 

MBCON 0 0 Boundary concentration conditions not 

modeled 

MFOG 0 0 Do not configure for FOG model output 

MREG 1 1 Technical options must conform to USEPA 

Long Range Transport (LRT) guidance 

Table G-5 CALPUFF Model Input Group 3: Species List-Chemistry Options

CSPEC Modeled1 Emitted 2

Dry 

Deposition3

Output Group 

Number 

SO2 1 1 1 0 

SO4

-2 1 0 2 0 

NOx 1 1 1 0 

HNO3 1 0 1 0 

NO3

-

 1 0 2 0 

NH3 0 0 1 0 

PM10

4

 1 1 2 0 

PMF4 1 1 2 0 

EC5 1 1 2 0 

SOA5 1 1 2 0 

Note 1: 0 = No, 1 = Yes 

Note 2: 0 = No, 1 = Yes (Depends on if species is being modeled or not) 

Note 3: 0 = none, 1 = computed gas, 2 = computed particle, 3 = user specified 

Note 4: Only PMF will be modeled and emitted in the no-obs run; however, both PM10

 and PMF will be modeled and emitted in the refined analysis 

Note 5: EC and SOA will not be modeled nor will it be emitted during the no-obs and 

 the refined runs

ASHDOWN-Q8-000694
G-4 

Table G-6 CALPUFF Model Input Group 4: Map Projection and Grid Control Parameters 

Parameter Default Value Comments 

PMAP UTM LCC Map Projection 

FEAST 0.0 0.000 False Easting 

FNORTH 0.0 0.000 False Northing 

RLATO None 40N Latitude and Longitude of projection origin 

RLONO None 97W Latitude and Longitude of projection origin 

XLAT1 None 33N Matching parallel of latitude for map 

projection 

XLAT2 None 45N Matching parallel of latitude for map 

projection 

DATUM WGS-84 WGS-G Datum region for output coordinates 

NX None 366 Number of X grid cells in meteorological 

grid 

NY None 234 Number of Y grid cells in meteorological 

grid 

NZ None 10 Number of vertical layers in meteorological 

grid 

DGRIDKM None 6 Grid spacing (km) 

ZFACE None 0, 20 40, 80, 160, 

320, 640, 1200, 

2000, 3000, 4000 

Cell face heights in meteorological grid (m) 

XORIGKM None -1008 Reference X coordinate for SW corner of 

grid cell (1,1) of meteorological grid (km) 

YORIGKM None -864 Reference Y coordinate for SW corner of 

grid cell (1,1) of meteorological grid (km) 

IBCOMP None 1 X index of lower left corner of the 

computational grid 

JBCOMP None 1 Y index of lower left corner of the 

computational grids 

IECOMP None 366 X index of the upper right corner of the 

computational grid 

JECOMP None 234 Y index of the upper right corner of the 

computational grid 

LSAMP T F Sampling grid is not used 

IBSAMP None 1 X index of lower left corner of the sampling 

grid 

JBSAMP None 1 Y index of lower left corner of the sampling 

grid 

IESAMP None 366 X index of upper right corner of the 

sampling grid 

JESAMP None 234 Y index of upper right corner of the 

sampling grid 

MESHDN 1 1 Nesting factor of the sampling grid 

ASHDOWN-Q8-000695
G-5 

Table G-7 CALPUFF Model Input Group 5: Output Options

Parameter Default Value Comments 

ICON 1 1 Output file CONC.DAT containing concentrations is 

created 

IDRY 1 1 Output file DFLX.DAT containing dry fluxes is 

created 

IWET 1 1 Output file WFLX.DAT containing wet fluxes is 

created 

IVIS 1 1 Output file containing relative humidity data is created 

LCOMPRS T T Perform data compression in output file 

IMFLX 0 0 Do not calculate mass fluxes across specific 

boundaries 

IMBAL 0 0 Mass balances for each species not reported hourly 

ICPRT 0 1 Print concentration fields to the output list file 

IDPRT 0 0 Do not print dry flux fields to the output list file 

IWPRT 0 0 Do not print wet flux fields to the output list file 

ICFRQ 1 1 Concentration fields are printed to output list file every 

hour (hr) 

IDFRQ 1 1 Dry flux fields are printed to output list file every 1 

hour 

IWFRQ 1 1 Wet flux fields are printed to output list file every 1 

hour 

IPRTU 1 3 Units for line printer output are in g/m3 for 

concentration and g/m2

/s for deposition 

IMESG 2 2 Messages tracking the progress of run written to 

screen 

LDEBUG F F Logical value for debug output 

IPFDEB 1 1 First puff to track 

NPFDEB 1 1 Number of puffs to track 

NN1 1 1 Meteorological period to start output 

NN2 10 10 Meteorological period to end output 

ASHDOWN-Q8-000696
G-6 

Table G-8 CALPUFF Model Input Group 6: Sub-Grid Scale Complex Terrain Input

Parameter Default Value Comments 

NHILL 0 0 Number of terrain features 

NCTREC 0 0 Number of special complex terrain receptors 

MHILL - 2 Input terrain and receptor data for CTSG hills input 

in CTDM format 

XHILL2M 1 1 Conversion factor for changing horizontal 

dimensions to meters 

ZHILL2M 1 1 Conversion factor for changing vertical dimensions 

to meters 

XCTDMKM None 0.0 E+00 X origin of CTDM system relative to CALPUFF 

coordinate system (km) 

YCTDMKM None 0.0 E+00 Y origin of CTDM system relative to CALPUFF 

coordinate system (km) 

Table G-9 CALPUFF Model Input Group 7: Dry Deposition Parameters for Gases 

Species Default Value Comments 

0.1509 0.1509 Diffusivity 

1000. 1000. Alpha star 

8.0 8.0 Reactivity 

0.0 0.0 Mesophyll resistance 

SO2

0.04 0.04 Henry’s Law coefficient 

0.1656 0.1656 Diffusivity 

1.0 1.0 Alpha star 

8.0 8.0 Reactivity 

5.0 5.0 Mesophyll resistance 

NOx

3.5 3.5 Henry’s Law coefficient 

0.1628 0.1628 Diffusivity 

1.0 1.0 Alpha star 

18.0 18.0 Reactivity 

0.0 0.0 Mesophyll resistance 

8.0E-8 8.0E-8 Henry’s Law coefficient 

HNO3

0.000359 0.000359 Henry’s Law coefficient 

ASHDOWN-Q8-000697
G-7 

Table G-10 CALPUFF Model Input Group 8: Dry Deposition Parameters for Particles

Species Default Value Comments 

SO4

-2 0.48 0.48 Geometric mass mean diameter of SO4-2 (μm) 

NO3

-

 2.0 0.48 Geometric mass mean diameter of NO3

- (μm) 

PM10 2.0 6.0 Geometric mass mean diameter of PMC (μm) 

PMF 2.0 0.48 Geometric mass mean diameter of PMF (μm) 

EC 2.0 0.48 Geometric mass mean diameter of EC (μm) 

SOA 0.48 0.48 Geometric mass mean diameter of SOA (μm) 

(Geometric Standard Deviation for all species assumed to be 2.0 μm). 

Table G-11 CALPUFF Model Input Group 9: Miscellaneous Dry Deposition Parameters

Parameter Default Value Comments 

RCUTR 30 30 Reference cuticle resistance (s/cm) 

RGR 10 10 Reference ground resistance (s/cm) 

REACTR 8 8 Reference pollutant reactivity 

NINT 9 9 Number of particle size intervals for effective 

particle deposition velocity 

IVEG 1 1 Vegetation in non-irrigated areas is active and 

unstressed 

Table G-12 CALPUFF Model Input Group 10: Wet Deposition Parameters

Species Default Value Comments 

3.21E-05 3.21E-05 Scavenging coefficient for liquid precipitation (s-1 SO2 ) 

0.0 0.0 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 SO4 ) -2 

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

6.0E-05 6.0E-05 Scavenging coefficient for liquid precipitation (s-1 HNO3 ) 

0.0 0.0 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 NO3 ) -

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

8.0E-05 8.0E-05 Scavenging coefficient for liquid precipitation (s-1 NH3 ) 

0.0 0.0 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 PM10 ) 

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 PMF ) 

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 EC ) 

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation (s-1 OC ) 

3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation (s-1) 

 

ASHDOWN-Q8-000698
G-8 

Table G-13 CALPUFF Model Input Group 11: Chemistry Parameters

Parameter Default Value Comments 

MOZ 1 1 Read ozone background concentrations from 

ozone.dat file (measured values). 

BCKO3 12*80 12*40 Background ozone concentration (ppb) 

BCKNH3 12*10 12*3 Background ammonia concentration (ppb) 

RNITE1 0.2 0.2 Nighttime NO2 loss rate in percent/hour 

RNITE2 2 2 Nighttime NOX loss rate in percent/hour 

RNITE3 2 2 Nighttime HNO3 loss rate in percent/hour 

MH202 1 1 Background H2O2 concentrations (Aqueous 

phase transformations not modeled) 

BCKH202 1 1 Background monthly H2O2 concentrations 

(Aqueous phase transformations not 

modeled) 

ASHDOWN-Q8-000699
G-9 

Table G-14 CALPUFF Model Input Group 12: Dispersion/Computational Parameters

Parameter Default Value Comments 

SYDEP 550 550 Horizontal size of a puff in meters beyond 

which the time dependant dispersion equation 

of Heffter (1965) is used 

MHFTSZ 0 0 Do not use Heffter formulas for sigma z 

JSUP 5 5 Stability class used to determine dispersion 

rates for puffs above boundary layer 

CONK1 0.01 0.01 Vertical dispersion constant for stable 

conditions 

CONK2 0.1 0.1 Vertical dispersion constant for neutral/stable 

conditions 

TBD 0.5 0.5 Use ISC transition point for determining the 

transition point between the Schulman-Scire to 

Huber-Snyder Building Downwash scheme 

IURB1 10 10 Lower range of land use categories for which 

urban dispersion is assumed 

IURB2 19 19 Upper range of land use categories for which 

urban dispersion is assumed 

ILANDUIN 20 * Land use category for modeling domain 

XLAIIN 3.0 * Leaf area index for modeling domain 

ZOIN -0.25 * Roughness length in meters for modeling 

domain 

ELEVIN 0.0 * Elevation above sea level 

XLATIN -999 - North latitude of station in degrees 

XLONIN -999 - South latitude of station in degrees 

ANEMHT 10 10 Anemometer height in meters 

ISIGMAV 1 1 Sigma-v is read for lateral turbulence data 

IMIXCTDM 0 0 Predicted mixing heights are used 

XMXLEN 1 1 Maximum length of emitted slug in 

meteorological grid units 

XSAMLEN 1 10 Maximum travel distance of slug or puff in 

meteorological grid units during one sampling 

unit 

MXNEW 99 60 Maximum number of puffs or slugs released 

from one source during one time step 

MXSAM 99 60 Maximum number of sampling steps during one 

time step for a puff or slug 

NCOUNT 2 2 Number of iterations used when computing the 

transport wind for a sampling step that includes 

transitional plume rise 

SYMIN 1 1 Minimum sigma y in meters for a new puff or 

slug 

SZMIN 1 1 Minimum sigma z in meters for a new puff or 

slug 

SVMIN .50 .50 Minimum lateral turbulence velocities (m/s) 

SWMIN 0.20, 0.12, 

0.08, 0.06, 

0.20, 0.12, 

0.08, 0.06, 

Minimum vertical turbulence velocities (m/s) 

ASHDOWN-Q8-000700
G-10 

0.03, 0.016 0.03, 0.016 

WSCALM 0.5 0.5 Minimum non-calm wind speeds (m/s) 

XMAXZI 3000. 3000. Maximum mixing height (m) 

XMINZI 50. 20. Minimum mixing height (m) 

SL2PF 10. 10. Maximum Sy/puff length 

PLXO 0.07, 0.07, 

0.10, 0.15, 

0.35, 0.55 

0.07, 0.07, 

0.10, 0.15, 

0.35, 0.55 

Wind speed power-law exponents 

WSCAT 1.54, 3.09, 

5.14, 8.23, 

10.80 

1.54, 3.09, 

5.14, 8.23, 

10.80 

Upper bounds of 1st 5 wind speed classes 

PGGO 0.020, 0.035 0.020, 0.035 Potential temp gradients PG E & F (deg/km) 

CDIV 0.01 0.01 Divergence criterion for dw/dz (1/s) 

PPC 0.5, 0.5, 0.5, 

0.5, 0.35, 

0.35 

0.5, 0.5, 0.5, 

0.5, 0.35, 

0.35 

Plume path coefficients (only if MCTADJ=3) 

NSPLIT 3 3 Number of puffs when puffs split 

IRESPLIT - 1900 Hour(s) when puff is eligible to split 

ZISPLIT 100 100 Previous hour’s minimum mixing height, m 

ROLDMAX 0.25 0.25 Previous Max mixing height/current mixing 

height ratio, must be less than this value to 

allow puff to split 

NSPLITH 5 5 Number of puffs resulting from a split 

SYSPLITH 1.0 1.0 Minimum sigma-y of puff before it may split 

SHSPLITH 2.0 2.0 Minimum puff elongation rate from wind shear 

before puff may split 

CNSPLITH 1.0E-07 1.0E-07 Minimum species concentration before a puff 

may split 

EPSSLUG 1.0E-04 1.0E-04 Criterion for SLUG sampling 

EPSAREA 1.0E-06 1.0E-06 Criterion for area source integration 

DSRISE 1.0 1.0 Trajectory step length for numerical rise 

algorithm 

Note: Values indicated by an asterisk (*) were allowed to vary spatially across the domain 

and were obtained from CALMET 

Table G-15 CALPUFF Model Input Group 13: Point Source Parameters

Parameter Default Value Comments 

NPT1 None Varies by 

scenario 

Number of point sources with stack parameters 

IPTU 1 1 Units for point source emission rates are g/s 

NSPT1 0 0 Number of source-species combinations with 

variable emissions scaling factors 

NPT2 None 0 Number of point sources with variable emission 

parameters provided in external file 

MISC None Point source 

parameters and 

emission data 

Point source inputs include stack height (H), 

stack diameter (d), exit temperature (T), exit 

velocity (v) emissions by species, and 

coordinate of stack (LCC) 

ASHDOWN-Q8-000701
G-11 

Table G-16 CALPUFF Model Input Group 17: Discrete Receptor Information 

Parameter Default Value Comments 

NREC None 427 Number of discrete receptors 

Please note that ADEQ will not be modeling area, line and volume sources which are 

input groups 14, 15, and 16 respectively. 

ASHDOWN-Q8-000702
H-1 

Appendix H. POSTUTIL Input Control Parameters 

Table H-1 Input Groups in the POSTUTIL Processor Control File 

Sub 

Group Description Applicable 

0a Input and output file names Yes 

1 NMET – Number of CALMET data files (365) Yes 

2 NFILES – Number of CALPUFF data files Yes 

Table H-2 POSTUTIL Processor Input Group 1: General Run Control Parameters 

Parameter Default Value Comments 

ISYR None See note 1 

below 

Starting year 

ISMO None 1 Starting month 

ISDY None 1 Starting day 

ISHR None 0 Starting hour 

NPER None See note 2 

below 

Number of periods to process 

NSPECINP None See note 3 

below 

Number of CALPUFF species to process 

NSPECOUT None See note 3 

below 

Number of species to output 

NSPECCMP None 0 Number of species to derive 

MDUPLCT None 1 Stop run if duplicate name 

NSCALED None 0 Number of CALPUFF files to ‘scale’ 

MNITRATE None 1 Re-compute the HNO3/NO3 partition for CALPUFF 

modeled concentrations? 1 = yes for all sources 

combined 

BCKNH3 10. 3. Default NH3 concentration (ppb) for HNO3/NO3

partitioning 

Note 1: Enter the modeled year for the CALPUFF run 

Note 2: Enter 8760 for years 2001 and 2002, but enter 8748 for the year 2003 

Note 3: Enter 6 for the no-obs run and 7 for the refined run 

ASHDOWN-Q8-000703
H-2 

Table H-3 POSTUTIL Processor Input Group 2: Species Processing Information 

Parameter Default Value Comments 

ASPECI None SO2, SO4, NOx, HNO3, NO3, 

PM10, PMF See Note 1 

Below 

Species to post-process 

ASPECO None SO2, SO4, NOx, HNO3, NO3, 

PM10, PMF See Note 4 

Below 

Species to output 

CSPECCMP None CSPECCMP = N 

SO2 = 0.0 

SO4 = 0.291667 

NO = 0.466667 

NO2 = 0.304348 

HNO3 = 0.222222 

NO3 = 0.451613 

PM10 = 0.0 

Nitrogen species to be computed by 

scaling and summing one or more of 

the processed input species using the 

scaling factors for each of the 

NSPECINP input species 

CSPECCMP None CSPECCMP = S 

SO2 = 0.50 

SO4 = 0.333333 

NO = 0.0 

NO2 = 0.0 

HNO3 = 0.0 

NO3 = 0.0 

PM10 = 0.0 

Sulfur species to be computed by 

scaling and summing one or more of 

the processed input species using the 

scaling factors for each of the 

NSPECINP input species 

MODDAT None A (Default=1.0) 

SO2 = 1.1 

SO4 = 1.5 

HNO3 = 0.8 

NO3 = 0.1 

B (Default=0.0) 

SO2 = 0.0 

SO4 = 0.0 

HNO3 = 0.0 

NO3 = 0.0 

Each species in NSCALED 

CALPUFF data files may be scaled 

before processing (e.g., to change the 

emission rate for all sources modeled 

in the run that produced a data file). 

For each scaled species the scaling 

factors are A and B where x' = Ax + 

B. 

Note 4: In the no-obs run just enter PMF, but in the refined run enter PM10 and PMF 

ASHDOWN-Q8-000704
I-1 

Appendix I. CALPOST Input Control Parameters 

Table I-1Input Groups in the CALPOST Processor Control File

Group Description Applicable 

0 Input and output file names Yes 

1 General Run Control Parameters Yes 

2 Visibility Parameters Yes 

3 Output Options Yes 

Table I-2 CALPOST Processor Input Group 1: General Run Control Parameters 

Parameter Default Value Comments 

METRUN 0 1 1 = Run all met periods in CALPUFF data file 

ISYR None 2001, 2002, 

2003 

Starting year 

ISMO None 1 Starting month 

ISDY None 1 Starting day 

ISHR None 0 Starting hour 

NPER None See note 1 

below 

Number of periods to process 

NREP 1 1 Process every hour of data? Yes = 1 

ASPEC None VISIB Process species for visibility 

ILAYER 1 1 Layer/deposition code; 1 for CALPUFF concentrations 

A 0.0 0.0 Scaling factor, slope 

B 0.0 0.0 Scaling factor, intercept 

LBACK F F Add hourly background concentrations or fluxes 

MSOURCE 0 0 Process only total reported contribution 

LG F F Process gridded receptors 

LD F T Process discrete receptors 

LCT F F Process complex terrain receptors 

LDRING F F Report receptor ring results 

NDRECP -1 See note 2 

below 

To select the Class I area’s receptors enter *1 after the 

number of receptors otherwise enter *0 

IBGRID -1 -1 X index of LL corner of receptor grid 

JBGRID -1 -1 Y index of LL corner of receptor grid 

IEGRID -1 -1 X index of UR corner of receptor grid 

JEGRID -1 -1 X index of UR corner of receptor grid 

NGONOFF 0 0 Number of gridded receptor rows 

NGXRECP 1 0 Exclude specific gridded receptors, Yes = 0 

Note 1: Enter 8760 for the years 2001 and 2002, but enter 8748 for the year 2003. 

Note 2: CALPOST is to be run for each Class I area assessed. 

3⁄4 The following are the number of receptors for each Class I area being assessed: 

1. Caney Creek = 80 

2. Hercules-Glade = 47 

3. Mingo Wilderness = 80 

4. Sipsey = 148 

5. Upper Buffalo = 72 

ASHDOWN-Q8-000705
I-2 

Table I-3 CALPOST Processor Input Group 2: Species Processing Information 

Parameter Default Value Comments 

RHMAX 98 95 Maximum RH (%) used in particle growth curve 

LVSO4 T T Compute light extinction for sulfate? 

LVNO3 T T Compute light extinction for nitrate? 

LVOC T T Compute light extinction for organic carbon? 

LVMPC T T Compute light extinction for coarse particles? 

LVMPF T T Compute light extinction for fine particles? 

LVEC T T Compute light extinction for elemental carbon? 

LVBK T T Include background in extinction calculation? 

SPECPMC PMC PMC Coarse particulate species 

SPECPMF PMF PMF Fine particulate species 

EEPMC 0.6 0.6 Extinction efficiency for coarse particulates 

EEPMF 1.0 1.0 Extinction efficiency for fine particulates 

EEPMCBK 0.6 0.6 Extinction efficiency for coarse part. background 

EESO4 3.0 3.0 Extinction efficiency for ammonium sulfate 

EENO3 3.0 3.0 Extinction efficiency for ammonium nitrate 

EEOC 4.0 4.0 Extinction efficiency for organic carbon 

EESOIL 1.0 1.0 Extinction efficiency for soil 

EEEC 10.0 10.0 Extinction efficiency for elemental carbon 

MVISBK 2 6 Method 6 for background light extinction: 

Compute extinction from speciated PM measurements. FLAG 

RH adjustment factor applied to observed & modeled sulfate 

and nitrate 

BEXTBTBK -- 12 Background extinction for MVISBK=1 (1/Mm) 

RHFRAC -- 10 Percentage of particles affected by RH 

RHFAC 12*value Depends 

on Class I 

Area 

Extinction coefficients for modeled and background 

hygroscopic species computed using EPA (2003) monthly RH 

adjustment factors 

BKSEC 0.02 0.02 Eastern background elemental carbon βext 

BKSO4 0.23 0.23 Eastern background sulfate βext

BKNO3 0.10 0.10 Eastern background nitrate βext

BKPMC 3.00 3.00 Eastern background PMC βext

BKSOC 1.40 1.40 Easter background organic carbon βext

BKSSOIL 0.50 0.50 Eastern background soil βext

BKSEC 0.02 0.02 Eastern background elem. βext

BEXTRAY 10.0 10.0 Extinction due to Rayleigh scattering (1/Mm) 

ASHDOWN-Q8-000706
I-3 

Table I-4 CALPOST Processor Input Group 3: Output Options 

Parameter Default Value Comments 

LDOC F F Print documentation image 

IPRTU 1 3 Print output units (μg/m3) for concentrations and (μg/m2/sec) 

for deposition 

L1HR T F Report 1 hr averaging times 

L3HR T F Report 3 hr averaging times 

L24HR T T Report 24 hr averaging times 

LRUNL T F Report run-length (annual) averaging times 

LT50 T F Top 50 table 

LTOPN F F Top ‘N’ table 

NTOP 4 4 Number of ‘Top-N’ values at each receptor 

ITOP 1,2,3,4 1,2,3,4 Ranks of ‘Top-N’ values at each receptor 

LEXCD F F Threshold exceedances counts 

THRESH1 -1.0 -1.0 Averaging time threshold for 1 hr averages 

THRESH3 -1.0 -1.0 Averaging time threshold for 3 hr averages 

THRESH24 -1.0 -1.0 Averaging time threshold for 24 hr averages 

THRESHN -1.0 -1.0 Averaging time threshold for NAVG-hr averages 

NDAY 0 0 Accumulation period, days 

NCOUNT 1 1 Number of exceedances allowed 

LECHO F F Echo option 

LTIME F F Time series option 

LPLT F F Plot file option 

LGRD F F Use grid format instead of DATA format 

LDEBUG F F Output information for debugging? 

ASHDOWN-Q8-000707
J-1 

Appendix J. Chapter 3 of the CENRAP BART Modeling 

Guidelines (Tesche, et al, 2005) 

3.0 CALPUFF FORMULATION AND IMPLEMENTATION 

The RHR relates visibility attenuation to extinction coefficient (bext) which is a 

measure of light scattering and absorption due to atmospheric constituents. Values for 

bext are estimated using an empirically derived equation which relates the extinction 

coefficient to relative humidity and the following components of particulate matter mass: 

(a) sulfates (SO4); (b) nitrates (NO3); (c) organic carbon (OC); (d) elemental carbon (EC); 

(f) particulate matter (IP) (“crustal material”); and (g) coarse mass (CM) (i.e., PM10 – 

PM2.5). The BART guidance requires the use of modeled concentrations of these 

components, together with a “humidity correction factor”, to estimate values for bext on 

all days within a three year period. These estimates, when compared with naturally 

occurring background extinction, are used to determine whether a source is causing or 

contributing to visibility impairment and also to measure the effectiveness of emissions 

controls on the source aimed at mitigating such effects. EPA notes that secondary 

particulate matter constitutes an important fraction of PM2.5 and that the modeling 

requirements for secondary and primary particulate matter differ in their need to consider 

atmospheric chemistry and in the degree of spatial resolution needed for the modeling 

(EPA, 2001, pg 22). 

This chapter introduces the formulation of the CALPUFF modeling system. We 

summarize the model capabilities as described in the user’s manuals (Scire et al., 

2000a,b) and discuss the capabilities and limitations of the model. Equipped with this 

information, states and source operators can identify those situations for which screening 

and/or source-specific applications of CALPUFF are appropriate. 

In most cases, we expect that application of the CALPUFF system will be 

sufficient to meet the BART Rule requirements. For that subset of conditions requiring 

advanced methods, Chapter 5 provides details on full-science alternative models and 

available data bases for BART modeling. Such conditions might include a situation 

where the default modeling shows that a source just barely causes or contributes to 

visibility degradation or in negotiations over the final BART determination that weighs 

technical and economic feasibility against expected air quality benefits. In both 

situations, a more accurate estimate of a source’s impacts may be very important to 

source operators. 

3.1 Original Model Development 

 

The CALPUFF modeling system was originally developed as a component of a 

three-part modeling system sponsored by the California Air Resources Board (ARB) in 

the mid-1980s. The ARB sought to develop a new puff-based model, a new grid-based 

model and an improved meteorological processor that would support application of the 

two. CALGRID was the urban-scale photochemical grid model resulting from the 

ASHDOWN-Q8-000708
J-2 

project (Yamartino et al., 1992) comparable in science and capabilities to the Urban 

Airshed Model (UAM-IV) (Scheffe and Morris, 1993). The model formulation was 

aimed at overcoming the deficiencies in EPA’s steady-state Gaussian plume models that 

were routinely used in California for inert and linearly reactive materials (principally 

SO2) from elevated point sources. Thus, the CALGRID model was designed to treat the 

complexities of urban-scale photochemical processes while CALPUFF was formulated to 

treat the non-steady state transport, diffusion, linear reaction, and deposition of primary 

pollutants from point sources. CALPUFF was not designed to address photochemical 

oxidants or and secondary aerosol formation production processes in a scientifically 

rigorous manner. 

 

In recent years, CALPUFF and its meteorological pre-processor (CALMET) have 

been used in a range of regulatory modeling studies to address point source issues that 

include complexities posed by complex terrain, large source-receptor distances, 

parameterized chemical transformation and deposition, and issues related to Class I 

visibility impacts. These applications are more complex than the California ARB’s nonsteady-state, linear chemistry formulation of the mid-1980s. 

The CALPUFF modeling system has been adopted by the EPA as a guideline 

model for source-receptor distances greater than 50 km, and for use on a case-by-case 

basis in complex flow situations for shorter distances. It was recommended for Class I 

impact assessments by the FLM Workgroup (FLAG, 2000) and the Interagency 

Workgroup on Air Quality Modeling (IWAQM) (EPA, 1998). As directed in the BART 

guidance, CALPUFF is the primary modeling system for screening and source-specific 

BART applications in the CENRAP region. Thus, examination of the model’s 

formulation provides the context for assessing the extent to which it suitable for 

simulating the various physical processes and gas-phase, aerosol, and aqueous-phase 

chemical processes that influences visibility. 

3.2 CALPUFF Model Formulation 

The CALPUFF user’s guide (Scire et al., 2000a) depicts the modeling system as 

shown in Figure 3-1. CALMET is a diagnostic/interpolation model that provides 

meteorological inputs to CALPUFF. These fields include hourly-averaged threedimensional wind and temperature fields and two-dimensional fields of mixing heights 

and other meteorological parameters. CALMET uses routine surface and aloft 

meteorological observations and/or three-dimensional output from prognostic numerical 

models such as MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) to construct the 

meteorological inputs. Other inputs to the air quality program include emissions 

information, receptor locations, ancillary geophysical information, and estimated 

concentrations of ambient pollutants that are entrained by the modeled puffs as each is 

carried downwind. Tables 3-1 and 3-2 summarize the key features of the 

CALMET/CALPUFF models as described in the user’s guides. 

Two post-processor routines are included to facilitate cumulative source impacts 

(POSTUTIL) and estimates of light extinction and visibility attenuation at Class I 

ASHDOWN-Q8-000709
J-3 

receptors of interest (CALPOST). In particular, CALPOST contains several options for 

computing change in extinction and deciviews for visibility assessments while the 

POSTUTIL postprocessor includes options for summing contributions of individual 

sources or groups of sources to assess cumulative impacts. POSTUTIL also contains an 

empirical nitric acid-nitrate chemical equilibrium module to estimate the cumulative 

effects of ammonia consumption by background sources once the simulation is 

completed. 

 3.2.1 Model Concept and Governing Equations 

The starting point for the CALPUFF development was the choice of the 

fundamental reference system of which there are two: Eulerian and Lagrangian. 

Consistent with the original ARB design criteria, the Lagrangian (moving puff) reference 

system was chosen for CALPUFF. In the Eulerian approach, the behavior of pollutants is 

described relative to a fixed coordinate system. The Lagrangian reference frame, in 

contrast, relates the behavior of pollutants relative to a coordinate system that moves with 

the average wind. These two approaches yield different mathematical relationships for 

pollutant concentrations that are equally valid. The choice of which approach to adopt 

depends upon the specific design goals of the modeling system. 

 The advantages and drawbacks of each approach are thoroughly discussed in the 

literature (Tesche, 1983; Seinfeld and Pandis, 1998; Jacobson, 1999; Russell and Dennis, 

2000). One of the criticisms of early Eulerian grid models was their ‘over-dilution’ of 

point source emissions into the fixed grid cells; but for the past twenty years, this 

limitation has been overcome through with the development of sub-grid-scale, plume-ingrid algorithms (Seigneur, et al., 1981; Godowitch, 2004; Karamchandani et al., 2005; 

Emery and Yarwood, 2005) and the use of multi-scale nested grids (Russell and Dennis, 

2000). While the Lagrangian approach is conceptually simple, flexible, and 

computationally inexpensive, the governing equations are not directly applicable to 

situations involving non-linear chemical reactions (Seinfeld and Pandis, (1998) and it is 

awkward to handle a large number of sources realistically. 

 3.2.2 Transport and Dispersion 

Adopting the Lagrangian concept, CALPUFF simulates the transport, dispersion, 

linear chemical transformation, and deposition of individual puffs carried downwind by 

the three-dimensional fields generated by CALMET. The model’s implementation 

follows puffs from the near source region (a few tens of meters) to hundreds of 

kilometers downwind. Its puff-based formulation, in conjunction with three-dimensional 

hourly meteorological data, allow CALPUFF to simulate the effects of time- and spacevarying meteorological conditions on pollutants emitted from a variety of source types. 

The major features and options of the CALPUFF model are summarized below: 

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both 

incorporated into CALPUFF. An option is provided to use either model for all stacks, or 

make the choice on a stack-by-stack and wind sector-by-wind sector basis. Both 

ASHDOWN-Q8-000710
J-4 

algorithms have been implemented in such a way as to allow the use of wind direction 

specific building dimensions. The PRIME building downwash model (Schulman et al., 

2000) is also included in CALPUFF as an option. 

Dispersion Coefficients: Turbulent dispersion in CALPUFF is treated with the K-theory 

(flux-gradient) closure scheme, defined for a Lagrangian frame of reference. Several 

options are provided in CALPUFF for the computation of dispersion coefficients, 

including the use of turbulence measurements (σv and σw), the use of similarity theory to 

estimate σv and σw from modeled surface heat and momentum fluxes, or the use of 

Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion 

equations based on the CTDM. Options are provided to apply an averaging time 

correction or surface roughness length adjustments to the PG coefficients. Recently, the 

EPA AERMOD dispersion parameters have been included in CALPUFF and are used 

regularly. 

Puff Sampling Functions: Puff sampling routines are included in CALPUFF to address 

computational difficulties encountered when applying a puff model to near-field releases. 

For near-field applications during rapidly-varying meteorological conditions, an 

elongated puff (slug) sampling function may be used. An integrated puff approach may 

be used during less demanding conditions. Both techniques reproduce continuous plume 

results under the appropriate steady state conditions. 

Wind Shear Effects: A key underpinning of the Lagrangian concept is that the modeled 

puffs retain their identity over the time- and spatial-scale associated with the effects the 

model is attempting to predict (i.e., visibility impairment at 200 km or beyond) While 

discrete puffs emitted from a source retain their physical integrity for a period of time, at 

some point the action of horizontal and vertical variations in wind speed and direction 

(i.e. ‘wind shear’) shred the puff into multiple elements. These new puff parcels, 

composed of remnants of the old puff, continue to be diffused and dispersed by the wind. 

The point where significant puff shredding occurs is difficult to define since it depends 

substantially upon the complexity of the meteorological conditions and the underlying 

terrain. But when shredding occurs, the Lagrangian concept in CALPUFF breaks down. 

By ignoring puff shredding (i.e., by keeping puffs intact), the model will systematically 

over-predict pollutant concentrations. 

To deal with this conceptual limitation, CALPUFF contains an optional puff 

splitting algorithm to simulate vertical wind shear effects across individual puffs. 

Differential rates of dispersion and transport among the “new” puffs generated from the 

original, well-mixed puff act to increase the effective rate of horizontal spread of the 

material as would be expected in the real atmosphere. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size to account for wind 

shear across the puffs. Detailed guidance on when and how the puff-splitting algorithm 

should be used and actual verification studies demonstrating that the technique operates 

as intended are not discussed in the model documentation or presented in the science 

literature.

ASHDOWN-Q8-000711
J-5 

Complex Terrain: Effects of complex terrain on puff transport are derived from the 

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor 

locations are simulated using one of two algorithms that modify the puff-height (either 

that of ISCST3 or a general “plume path coefficient” adjustment), or an algorithm that 

simulates enhanced vertical dispersion derived from the weakly-stratified flow and 

dispersion module of the Complex Terrain Dispersion Model (CTDMPLUS) (Perry et al., 

1989). The puff-height adjustment algorithms rely on the receptor elevation (relative to 

the elevation at the source) and the height of the puff above the surface. The enhanced 

dispersion adjustment relies on the slope of the gridded terrain in the direction of 

transport during the time step. 

Subgrid Scale Complex Terrain (CTSG): An optional module, CTSG treats terrain 

features that are not resolved by the gridded terrain field, and is based on the 

CTDMPLUS (Perry et al., 1989). Plume impingement on subgrid-scale hills is evaluated 

at the CTSG subgroup of receptors using a dividing streamline height (Hd) to determine 

which pollutant material is deflected around the sides of a hill (below Hd) and which 

material is advected over the hill (above Hd). The local flow (near the feature) used to 

define Hd is taken from the gridded CALMET fields. As in CTDMPLUS, each feature is 

modeled in isolation with its own set of receptors. 

Overwater and Coastal Interaction Effects: The CALMET processor contains overwater 

and overland boundary layer parameterizations allowing certain of the effects of water 

bodies on plume transport, dispersion, and deposition to be estimated. In a sense, 

CALPUFF operates as a hybrid model, by utilizing gridded fields of meteorology and 

dispersion conditions as well as grid-based descriptions of underlying land use. This 

includes the abrupt changes that occur at the coastline of a major body of water. 

Dry Deposition: A resistance model is used for the computation of dry deposition rates 

of gases and particulate matter as a function of geophysical parameters, meteorological 

conditions, and pollutant species. For particles, source-specific mass distributions may 

be provided for use in the resistance model. Of particular interest for BART analyses is 

the ability to separately model the deposition of fine particulate matter (< 2.5 μm 

diameter) from coarse particulate matter (2.5-10 μm diameter). 

Wet Deposition: An empirical scavenging coefficient approach is used to compute the 

depletion and wet deposition fluxes due to precipitation scavenging. The scavenging 

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen 

vs. liquid precipitation). 

 

 3.2.3 Primary Particulates 

CALPUFF is designed to simulate PM10 or PM2.5 or other user defined size 

distributions of particles. The smaller the particles, the more they disperse like an inert 

gas. In most cases, the dispersion of inert PM2.5 particles will differ only slightly from 

that of an inert gas. A key primary PM2.5 emission from coal-fired electric generating 

units (EGUs) of relevance to visibility calculations is particulate sulfate. Although 

ASHDOWN-Q8-000712
J-6 

primary sulfate emissions account for only a small fraction of the total sulfur emissions 

from such sources, it is appropriate to include their effect if reasonable estimates of 

primary sulfate emissions from the source are available. Treating primary sulfate 

emissions is likely to be most important at short distances from the stack before 

significant SO2 to secondary sulfate conversion has taken place. 

 3.2.4 Gas-Phase Chemistry 

Chemical reactions in the gas-phase play an important role in secondary aerosol 

formation by generating radical concentrations (e.g., the hydroxyl radical). These radical 

species oxidize SO2 and NOx, providing the precursors to aqueous–phase chemistry (i.e., 

chemistry in liquid water droplets) that convert SO2 to sulfate (e.g., H2O2 and O3), and 

form condensable gases from some volatile organic compounds (VOCs) that can then 

condense into particulate secondary organic aerosols (SOA). The levels of NOx, VOC, 

and O3 concentrations along with the reactivity of the VOCs, sunlight, temperature, and 

water vapor are all key variables that influence the radical cycle and consequent sulfate 

and nitrate formation rates. 

CALPUFF neglects realistic gas-phase processes entirely. The chemistry in 

CALPUFF parameterizes chemical transformation effects using five species (SO2, SO4

=

, 

NOx, HNO3, and NO3

-

) via a set of user-specified, diurnally-varying transformation rates. 

The model estimates secondary fine particulate matter (sulfate and nitrate) from 

emissions of gas-phase SO2 and NOx. Rather than simulating important non-linear gas 

phase oxidant chemistry, the model employs a user-supplied hourly ozone concentration 

as a surrogate for the hydroxyl radical and other oxidizing radical species. Ambient 

ammonia concentrations are also a user input along with temperature and relative 

humidity. 

Although simplifications of photochemistry have been attempted in the past, 

correct representation of the gas-phase photochemistry and the radical cycles are 

critically important in order to properly characterize sulfate and nitrate formation in the 

real atmosphere. Seigneur et al., (2000) demonstrated this fact in their evaluation of fullscience representations of photochemistry against simplified representations (but more 

advanced than CALPUFF). They concluded that simplified linearized transformation 

schemes are inadequate for describing sulfate and nitrate formation processes: 

“These results indicate that the accurate prediction of source-receptor 

relationships for PM2.5 requires a comprehensive treatment of PM2.5 formation 

from gaseous precursors for the secondary components of PM2.5 and a spatially 

resolved treatment of transport processes for primary PM2.5. Simplified 

treatments of either atmospheric chemistry or transport are appropriate only when 

the secondary or primary components of PM2.5, respectively, are not significant. 

Therefore, the development of source-receptor relationships for PM2.5 should be 

based on air quality models that provide comprehensive descriptions of 

atmospheric chemistry and transport.” 

ASHDOWN-Q8-000713
J-7 

Morris et al., (1998) also compared the sulfate and nitrate particulate estimates from a 

comprehensive full-science regional model with those from a model incorporating a 

simplified empirical chemical mechanism developed in a manner similar to the 

mechanism in CALPUFF. Evaluating the full-science and empirical chemistry models 

against observed concentrations, Morris and co-workers concluded: 

“Given the importance of the radical cycle for determining secondary PM 

formation rates, it appears that empirical gas-phase algorithms are inadequate for 

determining secondary PM formation.” 

The uncertainty and potential biases introduced into the CALPUFF visibility estimates 

due to neglect of gas phase oxidant chemistry remain unknown. 

3.2.5 Aerosol Chemistry 

 

Formation of secondary fine particulate matter (e. g., nitrates, sulfates, organic 

aerosols) in point source plumes is strongly dependent on the rate of mixing with ambient 

(background) air and the chemical composition of this background. The rates of 

oxidation of sulfur dioxide (SO2) and nitrogen dioxide (NO2) to sulfate and nitric acid can 

be very different within a power plant or industrial plume compared to that in the 

background air (Gillani and Godowitch, 1999; Karamchandani et al., 2000). Similarly, 

the formation of secondary organic aerosols from emitted VOCs and those from other 

anthropogenic and biogenic sources, adds yet another pathway in the formation of 

visibility-impairing aerosols. The presence of atmospheric ammonia introduces further 

nonlinearities into the gas phase and aerosol reactions. Accordingly, for a model to 

realistically simulate the production of secondary particulate sulfate, nitrate, and organic 

aerosols from a potential BART source, the mixing processes and chemical reactions 

within and outside of the plume must be treated realistically. If the chemical interactions 

between these two fundamentally different and interactive chemical environments are 

overly-simplified or neglected altogether, the ability of the model to correctly calculate 

plume concentrations, deposition, or visibility impacts is lost. 

Sulfate and Nitrate Formation. Two SO2 and NOx chemical transformation schemes are 

available in CALPUFF: the MESOPUFF-II algorithm (Scire et al., 1983; Atkinson et al., 

1982) and the RIVAD algorithm (Latimer et al., 1986). These algorithms calculate 

sulfate and nitrate formation rates based on the puff concentrations, background 

environmental parameters provided by CALMET, and background ozone and ammonia 

concentrations provided as input by the user. SOA particulates are not treated by either 

mechanism. The parameters used are as follows (note that each method does not use all 

of these parameters). 

Puff Average Concentrations (from CALPUFF)

• NOx concentration 

• SO2 concentration 

Environmental Parameters (from CALMET)

ASHDOWN-Q8-000714
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• Temperature 

• Surface Relative Humidity (RH) 

• Atmospheric Stability 

• Solar Radiation 

Background Concentrations (User Input)

• Ozone (O3) 

• Ammonia (NH3) 

The MESOPUFF-II chemical transformation scheme is EPA’s recommended approach 

for Class I area impact assessment (IWAQM, 1998). It entails pathways for five active 

pollutants (SO2, SO4, NOx, HNO3, and NO3) as follows: 

k1

SO2 Æ SO4

k2

NOx Æ HNO3 (+RNO3) 

k3

NOx Æ HNO3

NH3

HNO3 (g) ÅÆ NO3 (PM) 

where, 

SO2 is the puff average sulfur dioxide concentration; 

NOx is the puff average oxides of nitrogen concentrations; 

SO4 is sulfate concentrations formed from the SO2; 

HNO3 is the nitric acid formed from the NOx; 

NO3 is the particulate nitrate that is in equilibrium with the nitric acid; and 

NH3 is the background ammonia concentration. 

Daytime Rates

k1 = 36 x R0.55 x [O3]

0.71 x S-1.29 + k1(aq) 

k1(aq) = 3 x 10-8 x RH4 (added to k1 above during the day) 

k2 = 1206 x [O3]

1.5 x S-1.41 x [NOx]-0.33

k3 = 1261 x [O3]

1.45 x S-1.34 x [NOx]-0.12 

Nighttime Rates

k1 = 0.20 (%/hr) 

k2 = 0.00 (%/hr) 

k3 = 2.00 (%/hr) 

with, 

ASHDOWN-Q8-000715
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k1 is the SO2 to SO4 gas-phase transformation rate (%/hr)

k1(aq) is the SO2 to SO4 aqueous-phase transformation rate (%/hr) 

k2 is the NOx to HNO3+RNO3 transformation rate (%/hr) 

k3 is the NOx to HNO3 (only) transformation rate (%/hr) 

S is the stability index ranging from 2 to 6 

(PGT class A&B=2, C=3, D=4, E=5, F=6) 

R is the total solar radiation intensity (kw/m2

) 

RH is the relative humidity (%) 

[O3] is the user provided background ozone concentrations (ppm) 

[NOx] is the plume average NOx concentration (ppm) 

NH3 is the user provided background ammonia concentrations 

Daytime chemical transformations are based on statistically analyzed hourly 

transformation rates (Scire et al., 1983) obtained from box model simulations using the 

Atkinson et al., (1982) photochemical mechanism. In this scheme, gas-phase oxidation of 

SO2 and NOx depends on the hydroxyl (OH) radical concentrations for which 

background ozone, solar intensity (R), and stability index are used as surrogates. At 

night, OH concentrations are much lower and default SO2 and NOx oxidation rates of 0.2 

%/hr and 2.0 %/hr are assumed. The k1(aq) sulfate formation rate is added to the k1 rate 

during the day as a surrogate for aqueous-phase sulfate formation which begins to assume 

importance above approximately 50% RH (~0.2 %/hr sulfate formation rate) and peaks at 

100% RH (3%/hr sulfate formation rate). 

The sulfate and nitrate formation rate equations used in the MESOPUFF II 

scheme were originally generated by developing regression equations for a few key 

variables on the results of 144 box model simulations that used the 1982 photochemical 

mechanism of Atkinson et al. These box model simulations varied ambient temperature, 

ozone concentration, sunlight intensity, VOC concentrations, atmospheric stability, and 

plume NOx concentrations as shown in Table 3-1. The actual environmental conditions 

used to generate the sulfate and nitrate transformation equations were extremely limited. 

For example, the transformation rates did not cover temperatures below 10 deg C (50 deg 

F) or cleaner rural atmospheric conditions with VOC concentrations less than 50 ppbC. 

The CALPUFF MESOPUFF-II chemistry clearly neglects several environmental 

parameters and chemical processes that are important in simulating sulfate and nitrate 

formation in NOX/SO2 emissions source plumes. In many cases these deficiencies lead to 

an overestimation bias of the source’s sulfate and nitrate impacts. Factors that lead such 

a bias include: 

Lack of Temperature Effects: Photochemistry is known to be highly temperature 

sensitive, as evidenced by the fact that elevated ozone concentrations tend to 

occur on hot summer days. Lower temperatures produce lower OH and other 

radical concentrations and consequently lower sulfate and nitrate formation rates. 

The CALPUFF sulfate and nitrate formation rates, however, do not adequately 

incorporate temperature effects. The MESOPUFF-II chemical transformation 

algorithm was developed under conditions with a minimum temperature of only 

ASHDOWN-Q8-000716
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10° C (50° F). Thus, under conditions colder than 10° C, CALPUFF will 

overpredict sulfate and nitrate formation rates and impacts. CALPUFF typically 

estimates maximum sulfate and visibility impacts during the late fall/early spring 

and winter months; these are the same months when the CALPUFF 

overestimation bias from not considering temperature effects will be greatest. In 

addition, under colder temperatures, NOx will be converted to peroxyacetyl 

nitrate (PAN) so that the NOx is no longer available to be converted to nitrate. 

Since the CALPUFF chemistry ignores the PAN sink for NOx, it will 

systematically overpredict nitrate impacts. 

Effects of NOx Emissions on Sulfate Chemistry: Downwind of a point source 

with significant NOx/SO2 emissions, high NOx and SO2 concentrations co-exist. 

Under high NOx concentrations, radical concentrations are greatly reduced, 

resulting in very low ozone, sulfate, and nitrate formation rates. This is due to the 

NOx inhibition effect on photochemistry whereby: (1) the titration of NO with 

ozone eliminates ozone and its source as a radical generator; and (2) the high NO2

concentrations eliminate the OH radical via the NO2 + OH reaction thereby 

effectively shutting down photochemistry. Thus, in a NOx/SO2 point source 

plume near the source, there will be very low OH radical and ozone 

concentrations and consequently very low sulfate and nitrate formation. Since the 

simple MESOPUFF-II transformation equations cannot account for the NOx 

effect on the sulfate formation, CALPUFF will tend to over-predict sulfate 

formation rate in a NOx/SO2 point source plume near the source, which in turn 

leads to overstating the sulfate formation rate. Because NOx/SO2 point sources 

are typically buoyant, they are frequently be emitted aloft in a stable layer where 

the high NOx concentrations and inhibited sulfate and nitrate formation rates 

could persist 100 km or more downwind. 

Aqueous-Phase Sulfate Formation Algorithm. CALPUFF’s MESOPUFF-II chemistry 

treats aqueous-phase sulfate formation solely as a function of relative humidity (RH), 

which actually has no direct affect on aqueous-phase sulfate formation chemistry. The 

CALPUFF MESOPUFF-II aqueous-phase sulfate formation rate ranges from values of 

approximately 0.2 %/hr at 50% RH to 3.0 %/hr at 100% RH. Relative humidity (RH) is a 

measure of the content of water vapor in the atmosphere. However, in reality aqueousphase sulfate formation will depend on the amount of atmospheric liquid water content 

(LWC) in cloud or fog droplets, the pH of the water droplets, and the level of H2O2, 

ozone, and SO2 concentrations. Accordingly, in the atmosphere, aqueous-phase sulfate 

formation chemistry is not affected by RH. Thus, the CALPUFF aqueous-phase 

chemistry parameterization is incorrect. Although under conditions of clouds and fog 

there will be high RH, the occurrence of high RH with very little or no clouds or fog can 

be quite frequent. 

In a liquid water droplet, the reaction of SO2 with H2O2 to form sulfate is 

essentially instantaneous and is usually limited by the amount of H2O2 present (i.e., 

oxidant limited) for a NOx/SO2 point source. Once the H2O2 is reacted away within the 

water droplet, sulfate formation via this pathway slows to the rate of H2O2 formation, 

ASHDOWN-Q8-000717
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which would be extremely slow to nonexistent in a large point source plume due to the 

scavenging of radicals by the high NOx concentrations. This introduces an inaccurate 

representation of sulfate formation in CALPUFF that creates uncertainties and bias in 

modeled visibility impacts. Whether this uncertainty results in an under- or overestimate 

of sulfate formation is difficult to determine since the approach is scientifically invalid. 

Under conditions of high RH and little clouds or little plume interaction with clouds, it 

will clearly overstate sulfate formation. However, under conditions of cloudy conditions 

with available photochemical oxidants (i.e., H2O2 and O3) and a dilute NOx/Sox point 

source plume, it may understate sulfate formation. Near large NOx/SO2 point source 

where the elevated NOx concentrations scavenge and limit photochemical oxidants, the 

MESOPUFF-II algorithm will likely overstates sulfate formation. 

Thus, the CALPUFF aerosol chemistry fails to account for many environmental 

parameters that are necessary to simulate sulfate and nitrate formation rates, including 

VOCs and their reactivity, temperature, liquid water content, and NOx concentrations. In 

their evaluations against full-science PM models and observations, Seigneur et al., (2000) 

and Morris et al., (1998) both independently found that the empirical chemistry modules, 

such as employed by CALPUFF, are inadequate for estimating sulfate and nitrate 

formation. These findings are supported by EPA’s PM2.5 and Regional Haze SIP 

modeling guidance (EPA, 2001) that recommends against using Lagrangian models such 

as CALPUFF for simulating secondary PM. 

 From the foregoing, it is clear that the CALPUFF chemical transformation 

algorithms neglect important chemical processes necessary to accurately estimate the 

sulfate and nitrate impacts due to SO2 and NOX emissions. Given that EPA recommends 

the model for BART determinations, a key question is “What is the influence of the 

simplified chemistry on modeled estimates of visibility impacts from BART sources? In 

some cases, the inadequacies in the CALPUFF chemistry algorithms may simply 

introduce broader uncertainties into the calculation of estimated sulfate and nitrate 

impacts. In many cases, however, the simplifications made in the CALPUFF description 

of chemical processes result in a systematic bias in the estimated concentrations and 

visibility impacts due to SO2 and NOX emissions sources. For large point sources that 

emit SO2 and NOx emissions, such as EGUs, petrochemical process heaters, cement plant 

kilns, etc., many of the limitations in the CALPUFF MESOPUFF-II SO2 and NOx 

transformation algorithms would result in an overestimation bias. While models that are 

systematically biased high (i.e., over-predict impacts) may be appealing to regulatory 

decision-makers because they are ‘conservative’, the overprediction tendency may well 

lead to unwarranted and excessive control of emissions from some sources. Thus, the 

tradeoff between simplicity and conservativism on the one hand and technical credibility 

and unbiased answers on the other is a key element in the negotiation of modeling 

protocols developed by the states or source operators. 

 3.2.6 Surface Removal 

An especially important contributor to particulate concentrations is the rate of 

deposition to the surface. PM2.5 particles, which have a mass median diameter around 0.5 

ASHDOWN-Q8-000718
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μm, have an average net deposition velocity of about 1 cm/min (or about 14 m/day) and 

thus the deposition of fine particles is not usually significant except for ground-level 

emissions. On the other hand, coarse particles (those PM10 particles larger than PM2.5) 

have an average deposition velocity of more than 1 m/min (or 1440 m/day), which is 

significant, even for emissions from elevated stacks. 

CALPUFF includes parametric representations of particle and gas deposition in 

terms of atmospheric, deposition layer, and vegetation layer “resistances” and, for 

particles, the gravitational settling speed. Gravitational settling, which is of particular 

importance for the coarse fraction of PM10, is accounted for in the calculation of the 

deposition velocity. Effects of inertial impaction (important for the upper part of the PM10

distribution) and Brownian motion (important for small, sub-micron particles) and wet 

scavenging are also addressed. The BART guidance recommends that fine particulate 

matter (less than 2.5 μm diameter), which has higher light extinction efficiency than 

coarse particulate matter (2.5-10 μm diameters), should be treated separately in the 

model. CALPUFF allows for user-specified size categories to be treated as separate 

species, which includes calculating size-specific dry deposition velocities for each size 

category. 

3.3 CALMET Meteorological Preprocessor 

The CALMET meteorological model consists of a diagnostic wind field module 

and micrometeorological modules for over-water and overland boundary layers. When 

modeling a large geographical area such as the CENRAP domain, the user has the option 

to use a Lambert Conformal Projection coordinate system to account for Earth’s 

curvature. The major features and options of the meteorological model are summarized in 

Table 3-1. The techniques used in the CALMET model are briefly described below. 

3.3.1 Boundary Layer Modules 

The CALMET processor contains two boundary layer modules for application to 

overland and overwater grid cells. 

Overland Boundary Layer Module: Over land surfaces, the energy balance method of 

Holtslag and van Ulden (1983) is used to compute hourly gridded fields of the sensible 

heat flux, surface friction velocity, Monin-Obukhov length, and convective velocity 

scale. Mixing heights are determined from the computed hourly surface heat fluxes and 

observed temperature soundings using a modified Carson (1973) method based on Maul 

(1980). The module also determines gridded fields of PGT stability class and hourly 

precipitation rates. 

Overwater Boundary Layer Module: The aerodynamic and thermal properties of water 

surfaces suggest that a different method is needed for estimating boundary layer 

parameters in the marine environment. A profile technique, using air-sea temperature 

differences, is used in CALMET to compute the micro-meteorological parameters in the 

marine boundary layer. An upwind-looking spatial averaging scheme is optionally 

ASHDOWN-Q8-000719
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applied to the mixing heights and three-dimensional temperature fields in order to 

account for important advective effects. 

3.3.2 CALMET Diagnostic Wind Field Module 

The CALMET wind model was constructed from two other meteorological 

models used in California in the late 1970s. One was the California Institute of 

Technology (CIT) mass consistent interpolation model described by Goodin et al., 

(1980). The other was the Complex Terrain Wind Model (CTWM) developed at Systems 

Applications, Inc. (Tesche and Yocke, 1978; Yocke and Liu, 1978). The CTWM terrain 

adjustments used to modify the flow fields were assembled in the 1970s as part of 

research into fire spread and avalanche forecasting in mountainous regions of California. 

Various heuristic algorithms were developed to approximate down slope drainage flows, 

terrain blocking and channeling (Geiger, 1965), thermal heat islands (Stern and Malkus, 

1953), surface friction retardation, capping by an elevated inversion and so on. These 

algorithms were based on empirical studies in wind tunnels, numerical modeling 

experiments, and field studies in the Alps, some dating back to the 1930s (Defant, 1933). 

Later work by Tesche et al., (1986), Kessler et al., (1987) and Douglas and Kessler 

(1988) integrated the CIT and CTWM modeling system into a single meteorological 

model that included algorithms to blend observational data with prognostic 

meteorological model output. The combined model was used extensively for urban-scale 

ozone studies throughout the U.S. prior to the switch to MM5 as the preferred 

meteorological model for SIP studies in the mid-1990s. 

The CALMET model development incorporated the main features of the CTWM 

and CIT wind model and significantly updated the physical parameterizations and 

improved model input/output (I/O) schemes (Scire et al., 2000a). Today, CALMET uses 

the CTWM two-step approach to the computation of the wind fields. In the first step, an 

‘initial-guess’ wind field is constructed and then adjusted to approximate the kinematic 

effects of terrain, slope flows, and terrain blocking. Currently, the gridded MM5 field is 

used as the initial guess prior to terrain-perturbation. The second step consists of an 

objective analysis procedure to blend the MM5 field with observational data to produce a 

final wind field. This introduction of observational data in the second step of the 

CALMET wind field development is optional. It is also possible to run the model in “no 

observations” (No-Obs) mode, which involves the use only of MM5 gridded data for the 

initial guess field followed by fine-scale terrain adjustments on the scale of the CALMET 

domain. 

Normally, the CALMET computational domain is specified to be at smaller grid 

spacing than the MM5 dataset used to initialize the initial guess field. For example, 

36/12 km MM5 data sets available for 2000-2003 over the CENRAP domain have been 

used to develop the 6 km CALMET grids shown in Figures 5-1 through 5-4. 

The current thermal, kinematic, and dynamic effects parameterized in CALMET, 

used in the first step of the windfield development, are as follows: 

ASHDOWN-Q8-000720
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Kinematic Effects of Terrain: The CTWM algorithms for kinematic effects (Liu and 

Yocke, 1980) is used to evaluate the influence of the terrain on the wind field. The initial 

guess field winds are used to compute a terrain-forced vertical velocity, subject to an 

exponential, stability-dependent decay function. The effects of terrain on the horizontal 

wind components are evaluated by applying a divergence-minimization scheme to the 

initial guess wind field. The divergence minimization scheme is applied iteratively until 

the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm (Defant, 1933) has been upgraded (Scire 

and Robe, 1997) based on the shooting flow algorithm of Mahrt (1982). This scheme 

includes both advective-gravity and equilibrium flow regimes. At night, the slope flow 

model parameterizes the flow down the sides of the valley walls into the floor of the 

valley, and during the day, upslope flows are parameterized. The magnitude of the slope 

flow depends on the local surface sensible heat flux and local terrain gradients. The slope 

flow wind components are added to the wind field adjusted for kinematic effects. 

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are 

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the 

Froude number at a particular grid point is less than a critical value and the wind has an 

uphill component, the wind direction is adjusted to be tangent to the terrain. 

3.4 Estimation of Regional Haze Contributions 

The default procedure for quantifying visibility impacts is described in 

several documents (IWAQM, 1998; FLAG, 2000). Implementation of these 

procedures in CALPUFF is described in the user’s documentation (Scire et al., 

2000b). Generally, ‘visibility’ may be quantified either by visual range (the 

greatest distance that a large object can be seen) or by the light extinction 

coefficient, which is a measure of the light attenuation per unit distance due to 

scattering and absorption by gases and particles. Visibility is impaired when light 

is scattered in and out of the line of sight and by light absorbed along the line of 

sight. The light extinction coefficient (bext) considers light extinction by scattering 

(bscat) and absorption (babs): 

 

b

ext

 = b

scat

 + b

abs

 

The scattering components of extinction (bscat) are represented by light scattering 

due to air molecules (i.e., Rayleigh scattering, brayleigh) and light scattering due to 

particles, bsp. The absorption components of extinction (babs) include light 

absorption due to gases (bag) and particles (bap). Furthermore, particle 

scattering, bsp, can be expressed by its components: 

 

b

sp

 = b

SO4

 + b

NO3

 + b

OC

 + b

SOIL

+ b

Coarse

 

where the chemical species and soot scattering coefficients are given as: 

ASHDOWN-Q8-000721
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b

SO4 = 3 [(NH4

)

2

SO

4

] f(RH) 

b

NO3 = 3 [NH4

NO

3

]f(RH) 

b

OC = 4 [OC] 

b

SOIL= [Soil] 

b

Coarse= 0.6 [Coarse Mass] 

 

b

ap = 10 [EC] 

 

The numeric coefficient at the beginning of each equation is the dry scattering or 

absorption efficiency in meters-squared per gram. The f(RH) term is a monthlyaverage relative humidity adjustment factor. The terms in the brackets are the 

estimated concentrations fro CALPUFF (or other model) in micrograms per cubic 

meter (μg/m3

). 

 

Finally, the total atmospheric extinction is estimated as: 

 

b

ext

 = b

SO4

 + b

NO3

 + b

OC

 + b

SOIL

+ b

Coarse

+ b

ap

+ b

rayleigh 

or, substituting in the above terms, 

 bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 

1[Soil] + (3-1) 

 + 0.6[Coarse Mass] + 10[EC] + bRay

This is the so-called IMPROVE extinction equation currently recommended by 

EPA (2003). Note that the sulfate (SO4) and nitrate (NO3) components are 

hygroscopic because their extinction coefficients depend upon relative humidity. 

The concentrations, in square brackets, are in μg/m3

 and bext is in units of Mm-1. 

The Rayleigh scattering term (bRay) has a default value of 10 Mm-1, as 

recommended in EPA guidance for tracking reasonable progress (EPA, 2003a). 

The effect of relative humidity variability on the extinction coefficients for SO4 and 

NO3 can be estimated in several ways, but following the EPA BART guidelines, 

the Class I area-specific monthly f(RH) values shown in Table 6-1 should be 

used. 

 

Modeled ground level concentrations of each of the above visibility 

impairing pollutants are used with the IMPROVE equation to deduce the 

extinction coefficient. The change in visibility (measured in terms of ‘deciviews’) 

is compared against background conditions. The delta-deciview, ∆dv, value is 

ASHDOWN-Q8-000722
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calculated from the source’s contribution to extinction, bsource, and background 

extinction, bbackground, as follows:

 

∆dv = 10 ln((bbackground

+ bsource)/ bbackground) 

 

The impact of a source is determined by comparing the ∆dv, or haze index (HI), for 

estimated natural background conditions with the impact of the source and without the 

impact of the source. If the ∆dv value is greater than the 0.5 dv threshold the source is 

said to contribute to visibility impairment and is thus subject to BART controls. 

CALPOST uses a previous IMPROVE f(RH) curve (FLAG, 2000) which differs 

slightly from the f(RH) now used by IMPROVE and EPA (2003), mainly at high relative 

humidity. Also, CALPOST sets the maximum RH at 98% by default (although the user 

can change it), while the EPA’s guidance now caps it at 95% (easily modified in the 

CALPUFF input file). 

For regional haze light extinction calculations, use of a plume-simulating model 

such as CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not 

visually discernible as a plume per se, but nevertheless its presence could alter the 

visibility through the background haze. The IWAQM Phase 2 report states that such 

conditions occur starting 30 to 50 km from a source. This is consistent with the BART 

guidance recommendation for using CALPUFF for source-receptor distances greater than 

50 km. But, CALPUFF is also recommended by EPA as an option that can be considered 

for shorter transport distances when the plume may in fact be discernible from the 

background haze. 

Apart from the chemistry issues discussed previously, there do not appear to be 

any major reasons why CALPUFF cannot be used for even shorter transport distances 

than 30 km, as long as the scale of the plume is larger than the scale of the output grid so 

that the maximum concentrations and the width of the plume are adequately represented 

and so that the sub-grid details of plume structure can be ignored when estimating effects 

on light extinction. The standard 1-km output grid that has been established for Class I 

area analyses should serve down to source-receptor distances somewhat under 30 km; 

how much closer than 30 km will depend on the topography and meteorology of the area 

and should be evaluated on a case-by-case basis with individual CENRAP State 

modelers. (For reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 

km of travel distance, assuming Pasquill-Gifford dispersion rates under neutral 

conditions.) 

3.4.1 CALPOST Methods 

Calculation of the impact of the simulated plume particulate matter component 

concentrations on light extinction is carried out in the CALPOST postprocessor. For 

BART applications, this processor is of considerable importance. 

ASHDOWN-Q8-000723
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CALPOST is used to process the CALPUFF outputs, producing tabulations that 

summarize the results of the simulations, identifying for example, the highest and secondhighest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light 

extinction and related measures of visibility (deciviews), reporting these for a 24-hour 

averaging time. The CALPOST processor contains several options for evaluating visibility 

impacts, including the method described in the BART guidance, which uses monthly 

average relative humidity values. CALPOST contains implementations of the IWAQMrecommended and FLAG-recommended visibility techniques and additional options to 

evaluate the impact of natural weather events (fog, rain and snow) on background visibility 

and visibility impacts from modeled sources. CALPOST uses Equation 3-1 to calculate 

the extinction increment due to the source of interest and provides various methods for 

estimating the background extinction against which the increment is compared in terms 

of percent or deciviews. 

For background extinction, the CALPOST processor contains seven techniques 

for computing the change in light extinction due to a source or group of sources (i.e., 

Methods 1 through 7). These are usually reported as 24-hour average values, consistent 

with EPA and FLM guidance. In addition, there are two techniques for computing the 

24-hour average change in extinction (i.e., as the ratio of 24-hour average extinctions, or 

as the average of 24-hour ratios). Method 2 is the current default, recommended by both 

IWAQM (EPA, 1998) and FLAG (2000) for source-specific. Method 6 is recommended 

by EPA’s BART guidance (70 FR 39162). 

In Method 2, user-specified, speciated monthly concentration values are used to 

describe the background. When applied to natural conditions, for which EPA’s default 

natural conditions concentrations are annual averages, the same component 

concentrations would have to be used throughout the year (unless potential refinements to 

those default values resulted in concentrations that vary during the year). Hourly 

background extinction is then calculated using these concentrations and hourly, sitespecific f(RH) from a 1993 IWAQM curve or, optionally, the EPA regional haze f(RH) 

curve.2

 Again the RH is capped at either 98% (default) or a user-selected value (most 

commonly at 95%). 

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s 

monthly climatologically representative values) are used in place of hourly values for 

calculating both the extinction impact of the source emissions and the background 

conditions extinction. Hourly source impacts, with the effect on extinction due to sulfates 

and nitrates calculated using the monthly-average relative humidity in f(RH), are 

compared against the monthly default natural background concentrations. Thus the 

monthly-averaged relative humidity is applied to the hygroscopic components (i.e., 

sulfate and nitrate) of both the source impact and the background extinction with Method 

6. 

 

2

 Note that the hourly-varying natural background extinction here is not consistent with that prescribed by 

the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH) 

that only varies monthly is to be used. Method 6 uses these monthly average humidity values. 

ASHDOWN-Q8-000724
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3.4.2 POSTUTIL 

The POSTUTIL processor allows the cumulative impacts of multiple sources 

from different simulations to be summed, including computing the difference between 

two sets of predicted impacts (useful for evaluating the benefits of BART controls). It 

also contains a chemistry module to evaluate the equilibrium relationship between nitric 

acid and nitrate aerosols. This capability allows the potential non-linear effects of 

ammonia scavenging by background sulfate and nitrate sources to be approximated in the 

formation of nitrate from an individual source. The processor can compute the impacts 

of individual sources or groups of sources on sulfur and nitrogen deposition into aquatic, 

forest and coastal ecosystems, thereby allowing changes in deposition fluxes resulting 

from changes in emissions to be quantified. 

The POSTUTIL processor attempts to overcome the bias introduced when 

CALPUFF assumes that the full background ammonia concentration is entrained into 

each discrete puff. For a single puff, this may be satisfactory, but the model 

overestimates the production of ammonium nitrate when multiple puffs co-exist and 

overlap. The POSTUTIL processor re-partitions the ammonia and nitric acid 

concentrations to conform to the ammonia-limiting processes influencing nitrate 

formation. Though based on recognized science, this approximate post-processing 

method is fundamentally dependent on reliable estimates of ambient NH3 at the Class I 

receptor of interest. 

3.4.3 Refined Extinction and Background Visibility Estimates 

EPA, the IMPROVE Steering Committee, and the RPOs are evaluating whether 

refinements are warranted to the methods recommended for calculating extinction and the 

default estimate of natural background visibility. Whether EPA will approve of any 

changes to the IMPROVE equation is uncertain at this time. Also, the responsibility for 

incorporating any changes to the algorithms in CALPUFF (e.g., new f(RH) curves) is 

unclear. If changes to these methods are recommended by EPA, CENRAP is encouraged 

to adopt them. However, details of the process for incorporation of any refinements to 

the IMPROVE equations in the CALPUFF system should be addressed in the State’s or 

source operators modeling protocol. 

3.5 Model Availability 

The EPA-approved version of the CALPUFF modeling system is available from 

Earth Tech, Inc., (http://www.src.com/calpuff/calpuff1.htm). The main models 

(CALMET, CALPUFF, and CALPOST), their GUIs, and many of the processors are 

available to download. One may also register to receive notices of model updates. The 

most recent update to the system (25 May 2005) is a new version of CALMM5 (MM5 

V3) that has been added to the Download BETA-Test page. This version of CALMM5 

processes MM5 Version 3 output data directly. 

ASHDOWN-Q8-000725
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Earth Tech offers CALPUFF training courses that include a description of the 

technical formulation of the models, overviews of each of the processor programs, and 

hands-on application of the models to several case study data sets. Attendees of the 

course receive a training notebook, a workbook of case study problems, exercises, and 

data sets, updates on recent and future model enhancements, and the latest (proprietary) 

versions of the models and Graphical User Interfaces (GUIs). Other third-party training 

courses and materials are also available. 

3.6 CALPUFF Evaluation Studies 

Tesche (2002, 2003) reviewed results of various CALPUFF evaluation studies 

and reached the following conclusions: 

> There is a paucity of model evaluation information for CALPUFF at 

scales of 50 to 200 km and beyond; 

> Based on the limited information available, CALPUFF may be able to 

give unbiased estimates of short-term (i.e., 3-10 hr) concentrations of nonreactive contaminants to within a factor of two (e.g. 200%) out to 

distances of about 200 km from a source. This level or uncertainty in a 

200 km radius around a source is increased if one examines CALPUFF’s 

predictions in a particular modeling cell (e.g., one containing a population 

center) at a specific hour as opposed to considering the question of bias 

generally over the entire 200 km region irrespective of location and time 

of occurrence; 

> For time periods of a day or less, CALPUFF is unable to produce reliable 

predictions of non-reactive concentrations at a specific location and time; 

> What limited experimental data do exist suggest that the accuracy and 

reliability of the model’s predictions degrade as the distance scale 

increases; 

> While the IWAQM recommendations on the range of applicability of the 

CALPUFF model (50 to 200 km) rests on very sparse model evaluation 

information, EPA’s suggestion that the model can be used for scales 

beyond 200 km, even with case-by-case approval, is not based on model 

evaluation data; and 

> For chemically reactive pollutants such as SO2, NOx, sulfate, nitrate, nitric 

acid, and other secondary reaction products, the testing of CALPUFF 

model over extended spatial scales (50 km and beyond) has not been 

attempted in a rigorous manner. 

Scire et al., (2001) report an evaluation of CALPUFF sulfate, nitrate, light 

extinction, and sulfur and nitrogen deposition at a Class I areas over a range of sourceASHDOWN-Q8-000726
J-20 

receptor distances. In this study, in which a large number of sources were modeled 

simultaneously, sulfate and nitrate predictions at the CASTNet monitoring site in 

Pinedale, Wyoming were evaluated against observations, and light extinction predictions 

were evaluated using transmissometer measurements. Wet sulfur and nitrogen 

predictions were compared to observations at several acid deposition monitoring sites. 

This study is especially relevant because it evaluates the performance of the model to 

predict variables of direct interest in Class I visibility analyses, such as sulfate and nitrate 

concentrations and light extinction coefficients 

More recently, Chang et al., (2003) reported an intercomparison of CALPUFF 

with two other transport and dispersion models with high resolution field data. CALPUFF 

predictions for inert SF6 were compared using two recent mesoscale field datasets: the 

Dipole Pride 26 (DP26) and the Overland Along-wind Dispersion (OLAD). Both field 

experiments involved instantaneous releases of sulfur hexafluoride tracer gas in a 

mesoscale region with desert basins and mountains. Tracer concentrations were observed 

along lines of samplers at distances up to 20 km. CALPUFF predictions were evaluated 

using the maximum 3-h dosage (concentration integrated over time) along a sampling 

line. At the DP26 sampler array, CALPUFF had mean biases within 35% and random 

scatters of about a factor of 3–4. About 50%–60% of the CALPUFF predictions were 

within a factor of 2 of the observations. At the OLAD site, the model underpredicted by 

a factor of 2–3, on average, with random scatters of a factor of 3–7. Only about 25%–

30% of the CALPUFF predictions of inert SF6 were within a factor of 2 of observations. 

The tracer studies with which CALPUFF transport and diffusion capabilities were 

evaluated in the IWAQM Phase 2 report were generally over distances greater than 50 

km. More recently, model performance has been performed at shorter distances including 

a power plant in Illinois in simple terrain at source-receptor distances in arcs ranging 

from 0.5 km to 50 km from the stack (Strimaitis et al., 1998). Another CALPUFF 

evaluation study over short-distances is reported by Morrison et al. (2003). These 

studies address model performance over source-receptor distances from a few hundred 

meters to 50 km. 

ASHDOWN-Q8-000727
J-21 

Figure 3-1. CALPUFF Modeling System Components. (Scire et al., 2000a) 

ASHDOWN-Q8-000728
J-22 

Table 3-1. Major Features of the CALMET Meteorological Model. (Scire et al., 

2000b)

 • Boundary Layer Modules of CALMET 

 - Overland Boundary Layer - Energy Balance Method 

 - Overwater Boundary Layer - Profile Method 

 - Produces Gridded Fields of: 

 -- Surface Friction Velocity 

 -- Convective Velocity Scale 

 -- Monin-Obukhov Length 

 -- Mixing Height 

 -- PGT Stability Class 

 -- Air Temperature (3-D) 

 -- Precipitation Rate 

 • Diagnostic Wind Field Module of CALMET

 - Slope Flows 

 - Kinematic Terrain Effects 

 - Terrain Blocking Effects 

 - Divergence Minimization 

 - Produces Gridded Fields of U, V, W Wind Components 

 - Inputs Include Domain-Scale Winds, Observations, and 

 (optionally) Coarse-Grid Prognostic Model Winds 

 - Lambert Conformal Projection Capability 

ASHDOWN-Q8-000729
J-23 

Table 3-2. Major Features of the CALPUFF Dispersion Model (Scire et al., 2000a) 

 • Source types

 - Point sources (constant or variable emissions) 

 - Line sources (constant or variable emissions) 

 - Volume sources (constant or variable emissions) 

 - Area sources (constant or variable emissions) 

 • Non-steady-state emissions and meteorological conditions

 - Gridded 3-D fields of meteorological variables (winds, temperature) 

- Spatially-variable fields of mixing height, friction velocity, convective 

velocity scale, 

 Monin-Obukhov length, precipitation rate 

 - Vertically and horizontally-varying turbulence and dispersion rates 

 - Time-dependent source and emissions data for point, area, and volume 

sources 

 - Temporal or wind-dependent scaling factors for emission rates, for all 

source types 

 • Interface to the Emissions Production Model (EPM)

 - Time-varying heat flux and emissions from controlled burns and 

wildfires 

 • Efficient sampling functions

 - Integrated puff formulation 

 - Elongated puff (slug) formulation 

 • Dispersion coefficient (σy, σz) options

 - Direct measurements of σv and σw

 - Estimated values of σv and σw based on similarity theory 

 - Pasquill-Gifford (PG) dispersion coefficients (rural areas) 

 - McElroy-Pooler (MP) dispersion coefficients (urban areas) 

 - CTDM dispersion coefficients (neutral/stable) 

 • Vertical wind shear

 - Puff splitting 

 - Differential advection and dispersion 

 • Plume rise

 - Buoyant and momentum rise 

 - Stack tip effects 

 - Building downwash effects 

 - Partial penetration 

 - Vertical wind shear 

 • Building downwash

ASHDOWN-Q8-000730
J-24 

 - Huber-Snyder method 

 - Schulman-Scire method 

 - PRIME method 

 

Table 3-2. Major Features of the CALPUFF Dispersion Model (Concluded). 

 • Complex terrain

 - Steering effects in CALMET wind field 

 - Optional puff height adjustment: ISC3 or "plume path coefficient" 

 - Optional enhanced vertical dispersion (neutral/weakly stable flow in 

CTDMPLUS) 

 • Subgrid scale complex terrain (CTSG option)

 - Dividing streamline, Hd, as in CTDMPLUS: 

 - Above Hd, material flows over the hill and experiences altered 

diffusion rates 

 - Below Hd, material deflects around the hill, splits, and wraps 

around the hill 

 • Dry Deposition 

 - Gases and particulate matter 

 - Three options: 

- Full treatment of space and time variations of deposition with a 

resistance model 

 - User-specified diurnal cycles for each pollutant 

 - No dry deposition 

 • Overwater and coastal interaction effects

 - Overwater boundary layer parameters 

 - Abrupt change in meteorological conditions, plume dispersion at 

coastal boundary 

 - Plume fumigation 

 • Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and 

NO3 

(MESOPUFF II method) 

 - Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2 HNO3, 

and NO3 (RIVAD/ARM3 method) 

 - User-specified diurnal cycles of transformation rates 

 - No chemical conversion 

ASHDOWN-Q8-000731
J-25 

 • Wet Removal

 - Scavenging coefficient approach 

 - Removal rate a function of precipitation intensity and precipitation type 

ASHDOWN-Q8-000732
J-26 

Table 3-3. Parameter Variations in Box Model Simulations Used to Develop the 

CALPUFF Sulfate and Nitrate Formation Algorithms. (Morris et al., 

2003).

Surrogate 

Parameter 

Number of 

Variations 

Model Input Parameters And Variations 

Season 3 Temperatures of 30, 20 and 10 °C were used for the, 

respectively, summer, fall and winter seasons. 

Diurnally varying clear skies solar radiation was 

assumed for each season corresponding to a latitude 

of 40°. 

Background Air 

Reactivity 

4 For the summer season the following four levels of 

background ozone and VOCs were used: 

Ozone 

(ppb) 

VOC 

(ppbC) 

20 50 

50 250 

80 500 

200 2,000 

For fall and winter the ozone concentrations were 

assumed to be 75% and 50% of the summer levels. 

Dispersion 2 Two different rates of plume dispersion were used: 

(1) a stable case with a wind speed of 1.5 m/s and; (2) 

a slightly unstable case with a wind speed of 5.0 m/s. 

Release Time 2 Photochemical box model simulations were 

performed with release times of sunrise and noon. 

Plume NOx 

Concentration 

3 Initial plume NOx concentrations of 7, 350 and 1400 

ppb were used. 

ASHDOWN-Q8-000733
Domtar Industries B-1 Trinity Consultants 

Ashdown Mill H:\2DJ\Domtar\P06_081\BART17_Submitted on 2007-03-27.doc

APPENDIX B – CONTROLS COST ANALYSES

ASHDOWN-Q8-000734
Costs ($)

Purchased Equipment Costa $ 1,050,000

Budgetary Qualifier (+/-25%) 262,500 

 1,312,500

Direct installation costs

Foundation and supports

Handling and erection

Electrical

Piping

Insulation

Painting

Direct installation Costs 656,250 

$ 1,968,750

=1.0 x Total Direct Cost

Engineering

Construction and field expenses

Contrator fees

Start-up

Performance test

Contingencies

Structural Modification (4%)

$ 1,968,750

$ 3,937,500

Operating Laborc 9,022

Operating Labor Supervisiond 1,353

Maintenance Labore 9,922

Maintenance Materialsf 9,922

Utilities - Electricityg 112,560

Utilities - Natural Gasl 3,108,000

Waste Treatment & Disposal

Total Direct Operating Costs $ 3,250,779

Indirect Operating Costs

Overheadh 18,131

Property Taxi 39,375

Insurancei 39,375

Administrationj 78,750

Capital Recovery (5% interest, 10 year life)k 509,924

$ 685,555

$ 3,936,334

Total Annualized Costs 3,936,334 

1084

0.5

542

$ 7,262

a

Based on the equipment cost estimate from Energy Systems Associates

b

Factored estimate based on recent capital project installations

c

Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d

Supervisor pay = 15% of Operator pay

e

Maintenance = 240 hours @ $41.34/hr

f

Maintenance Materials = 100% of Maintenance Labor

g

Electrical usage (335kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR combo

h

Overhead = 60% of Labor & Material

I

 =1% TCI (Total Capital Investment)

j

 =2% TCI (Total Capital Investment)

k =factor of 0.129504575 for 5% interest on 10 year life

l

 = Natural gas usage at $370/hr at $8/MMBTU

Estimated Average Cost ($/ton) of Methane DeNOx on No. 1 Power Boiler - NOx Control

CAPITAL COSTS

Direct Costs

Total Uncontrolled NOx Emissions (tpy)

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

OPERATING COSTS

Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy)

Cost/Ton Pollutant Removed

Total Direct Capital Cost = Equip Cost + 1.5*Equip Costb

Indirect Capital Costs

Total Indirect Capital Costsb

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal Effciency

ASHDOWN-Q8-000735
Costs ($)

Purchased Equipment Costa $ 2,980,000

Budgetary Qualifier (+/-25%) 745,000 

 3,725,000

Direct installation costs

Foundation and supports

Handling and erection

Electrical

Piping

Insulation

Painting

Direct installation Costs 1,862,500 

$ 5,587,500

=1.0 x Total Direct Cost

Engineering

Construction and field expenses

Contrator fees

Start-up

Performance test

Contingencies

Structural Modification (4%)

$ 5,587,500

$ 11,175,000

Operating Laborc 9,022

Operating Labor Supervisiond 1,353

Maintenance Labore 9,922

Maintenance Materialsf 9,922

Utilities - Electricityg 112,560

Utilities - Water 

Waste Treatment & Disposal

Total Direct Operating Costs $ 142,779

Indirect Operating Costs

Overheadh 18,131

Property Taxi 111,750

Insurancei 111,750

Administrationj 223,500

Capital Recovery (5% interest, 10 year life)k 1,447,214

$ 1,912,345

$ 2,055,123

Total Annualized Costs 2,055,123 

1084

0.4

434

$ 4,740

a

Based on the equipment cost estimate provided by Jansen Combustion and Boiler Technologies Inc.

b

Factored estimate based on recent capital project installations

c

Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/yea

d

Supervisor pay = 15% of Operator pay e

Maintenance = 240 hours @ $41.34/hr f

Maintenance Materials = 100% of Maintenance Labo

g

Electrical usage (335kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR combo h

Overhead = 60% of Labor & Material I

 =1% TCI (Total Capital Investment) j

 =2% TCI (Total Capital Investment)

k =factor of 0.129504575 for 5% interest on 10 year life

Pollutant Removed(tpy)

Cost/Ton Pollutant Removed

Total Direct Capital Cost = Equip Cost + 1.5*Equip Costb

Indirect Capital Costs

Total Indirect Capital Costsb

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal Effciency

Estimated Average Cost ($/ton) of OFA System Upgrade on No. 1 Power Boiler - NOx Control

CAPITAL COSTS

Direct Costs

Total Uncontrolled NOx Emissions (tpy)

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

OPERATING COSTS

Direct Operating Costs

Total Indirect Operating

ASHDOWN-Q8-000736
Costs ($)

Purchased Equipment Costa $ 1,200,000

Budgetary Qualifier (+/-25%) 300,000 

 1,500,000

Direct installation costs

Foundation and supports

Handling and erection

Electrical

Piping

Insulation

Painting

Direct installation Costs 750,000 

$ 2,250,000

=1.0 x Total Direct Cost

Engineering

Construction and field expenses

Contrator fees

Start-up

Performance test

Contingencies

Structural Modification (4%)

$ 2,250,000

$ 4,500,000

Operating Laborc 9,022

Operating Labor Supervisiond 1,353

Maintenance Labore 9,922

Maintenance Materialsf 9,922

Utilities - Electricityg 141,120

Utilities - Natural Gasl 4,401,600

Waste Treatment & Disposal

Total Direct Operating Costs $ 4,572,939

Indirect Operating Costs

Overheadh 18,131

Property Taxi 45,000

Insurancei 45,000

Administrationj 90,000

Capital Recovery (5% interest, 10 year life)k 582,771

$ 780,902

$ 5,353,840

Total Annualized Costs 5,353,840 

2514

0.5

1257

$ 4,259

a

Based on the equipment cost estimate from Energy Systems Associates

b

Factored estimate based on recent capital project installations

c

Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d

Supervisor pay = 15% of Operator pay

e

Maintenance = 240 hours @ $41.34/hr

f

Maintenance Materials = 100% of Maintenance Labor

g

Electrical usage (420kW) associated with running fans; from Energy Systems Associates estimate for OFA/FGR combo

h

Overhead = 60% of Labor & Material

I

 =1% TCI (Total Capital Investment)

j

 =2% TCI (Total Capital Investment)

k =factor of 0.129504575 for 5% interest on 10 year life

l

 = Natural gas usage at $524/hr at $8/MMBTU

Estimated Average Cost ($/ton) of Methane DeNOx on No. 2 Power Boiler - NOx Control

CAPITAL COSTS

Direct Costs

Total Uncontrolled NOx Emissions (tpy)

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

OPERATING COSTS

Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy)

Cost/Ton Pollutant Removed

Total Direct Capital Cost = Equip Cost + 1.5*Equip Costb

Indirect Capital Costs

Total Indirect Capital Costsb

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal Effciency

ASHDOWN-Q8-000737
Costs ($)

Purchased Equipment Costa $ 4,338,880

Budgetary Qualifier (+/-25%) 1,084,720 

 5,423,600

Direct installation costs

Foundation and supports

Handling and erection

Electrical

Piping

Insulation

Painting

Direct installation Costs 2,711,800 

$ 8,135,400

=1.0 x Total Direct Cost

Engineering

Construction and field expenses

Contrator fees

Start-up

Performance test

Contingencies

Structural Modification (4%)

$ 8,135,400

$ 16,270,800

Operating Laborc 9,022

Operating Labor Supervisiond 1,353

Maintenance Labore 9,922

Maintenance Materialsf 9,922

Utilities - Electricityg 141,120

Utilities - Water 

Waste Treatment & Disposal

Total Direct Operating Costs $ 171,339

Indirect Operating Costs

Overheadh 18,131

Property Taxi 162,708

Insurancei 162,708

Administrationj 325,416

Capital Recovery (5% interest, 10 year life)k 2,107,143

$ 2,776,106

$ 2,947,445

Total Annualized Costs 2,947,445 

2514

0.4

1006

$ 2,931

a

Scaled from quote for #1 PB based on six-tenths factor rule for cost estimation from Peters, Max S. and Timmerhaus, 

Klaus D., Plant Design and Economics for Chemical Engineers, Fourth Edition, McGraw-Hill, Inc., 1991, p. 169.

b

Factored estimate based on recent capital project installations

c

Operating labor = 0.75 hours/day @ $34.37/hr rate for 350 days/year

d

Supervisor pay = 15% of Operator pay

e

Maintenance = 240 hours @ $41.34/hr

f

Maintenance Materials = 100% of Maintenance Labor

g

Electrical usage assumption (420kW) associated with running auxiliary equipment; from Energy Systems Associates

 estimate for OFA/FGR combo

h

Overhead = 60% of Labor & Material

I

 =1% TCI (Total Capital Investment)

j

 =2% TCI (Total Capital Investment)

k =factor of 0.129504575 for 5% interest on 10 year life

Estimated Average Cost ($/ton) of OFA System Upgrade on No. 2 Power Boiler - NOx Control

CAPITAL COSTS

Direct Costs

Total Uncontrolled NOx Emissions (tpy)

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

OPERATING COSTS

Direct Operating Costs

Total Indirect Operating

Pollutant Removed(tpy)

Cost/Ton Pollutant Removed

Total Direct Capital Cost = Equip Cost + 1.5*Equip Costb

Indirect Capital Costs

Total Indirect Capital Costsb

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

Removal Effciency

ASHDOWN-Q8-000738
Costs ($)

Purchased Equipment Costa $ 1,800,000

Direct installation costs

Foundation and supports

Handling and erection

Electrical

Piping

Insulation

Painting

Direct installation Costs 900,000 

$ 2,700,000

=1.0 x Total Direct Cost

Engineering

Construction and field expenses

Contrator fees

Start-up

Performance test

Contingencies

Structural Modification (4%)

$ 2,700,000

$ 5,400,000

Operating Laborc 9,022

Operating Labor Supervisiond 1,353

Maintenance Labore 9,922

Maintenance Materialsf 9,922

Utilities - Electricityg 141,120

Utilities - Water 

Waste Treatment & Disposal

Total Direct Operating Costs $ 171,339

Indirect Operating Costs

Overheadh 18,131

Property Taxi 54,000

Insurancei 54,000

Administrationj 108,000

Capital Recovery (5% interest, 10 year life)k 699,325

$ 933,456

$ 1,104,795

Total Annualized Costs 1,104,795 

2514

0.3

754

$ 1,465

Estimated Average Cost ($/ton) of Low NOx Burners on No. 2 Power Boiler - NOx Control

CAPITAL COSTS

Direct Costs

Total Direct Capital Cost = Equip Cost + 1.5*Equip Costb

Indirect Capital Costs

Total Indirect Capital Costsb

TOTAL CAPITAL INVESTMENT (TCI = DC+IC)

OPERATING COSTS

Removal Effciency

Pollutant Removed(tpy)

Cost/Ton Pollutant Removed

Direct Operating Costs

Total Indirect Operating

TOTAL ANNUALIZED COSTS (TAC = DOC + IOC)

Total Uncontrolled NOx Emissions (tpy)

ASHDOWN-Q8-000739
Domtar Industries C-1 Trinity Consultants 

Ashdown Mill H:\2DJ\Domtar\P06_081\BART17_Submitted on 2007-03-27.doc

APPENDIX C – COMPLIANCE PLAN AND SCHEDULE

Domtar will implement control measures or other options for reducing emissions to comply with the 

proposed BART limits as expeditiously as possible and before the date five years after EPA approval 

of ADEQ’s BART State Implementation Plan (SIP), as required by Regional Haze Rule and BART 

Guidelines. 

Proposed BART Compliance Timeline 

May 14-20, 2007 Installation of WESP on No. 1 Power Boiler complete 

May 21, 2007 Tentative startup of WESP on No. 1 Power Boiler 

September 13, 2007 Boiler MACT Compliance Deadline 

March 11, 2008 Last day to show compliance with Boiler MACT = Date achieve BART 

Particulate Matter limits 

Early 2010 NOX Reduction Technology Evaluation 

Late 2011 NOX Reduction Technology Selected 

Late 2012 Installation of selected NOX Reduction Technology 

Early 2013 BART Compliance Deadline 

ASHDOWN-Q8-000740
Domtar Industries D-1 Trinity Consultants 

Ashdown Mill H:\2DJ\Domtar\P06_081\BART17_Submitted on 2007-03-27.doc

APPENDIX D – REQUESTED DESIGN DETAILS OF NO. 1 POWER BOILER

The No. 1 power boiler, built by Babcock & Wilcox in 1967, is a balanced draft, two drum sterling 

boiler designed to burn natural gas, fuel oil and bark for the production of steam. 

The No. 1 power boiler has a maximum continuous steam rating of 275,000 lbs/hr at 850 psig and 

850°F. The boiler discharges steam into the mill's 850# high pressure header system. 

The No. 1 power boiler is typically a swing boiler (adjusts its fuel firing rate) to follow the 850 psig 

header pressure. 

The fuel system consists of the three separate subsystems listed below that deliver combustible 

material into the boiler furnace. 

- Bark System - supplies bark, wood waste, pelletized paper fuel, tire-derived fuel and 

municipal yard waste from the woodyard area and distributes it onto the grate for 

burning. Bark is the primary fuel source for No. 1 power boiler. 

- Natural Gas System - supplies gas from the main mill pipeline to the boiler's six 

burners and ignitors. Natural gas is used to warm up the boiler during start-up and to 

supplement bark combustion to maintain load. 

- No. 6 Fuel Oil System - prepares and supplies No. 6 fuel oil, used oil generated on 

site or reprocessed fuel oil to the boiler's burners. Oil serves primarily as a backup to 

natural gas and is not normally fired. 

The combustion air system consists of the three subsystems listed below that provide the oxygen for 

fuel combustion. 

- Air Supply & Preheat System - provides a steady supply of combustion air at the 

necessary flows and temperatures to ensure efficient combustion. 

- Burner Air System - provides air for the combustion of natural gas and/or fuel oil at 

the burners. 

- Bark Air System - provides air for drying and burning of bark system fuels on the 

grates (undergrate air), combustion of bark system fuels above the grates (overfire 

air) and distribution of bark system fuels onto the grates (distribution air). 

The flue gas system consists of several components listed below that handle the by-products of 

combustion. 

- Tubular Air Heater - transfers heat from the flue gas to the combustion air. 

ASHDOWN-Q8-000741
Domtar Industries D-2 Trinity Consultants 

Ashdown Mill H:\2DJ\Domtar\P06_081\BART17_Submitted on 2007-03-27.doc

- Mechanical Dust Collector - removes environmentally harmful particulate from the 

flue gas prior to atmospheric discharge. 

- ID Fan - removes the flue gas from the furnace at a controlled rate to maintain a 

balanced draft. 

- Stack - discharges the flue gas to atmosphere. 

- Sootblowers - clean the tube surfaces of ash and slag deposited from the flue gas. 

The No. 1 Power Boiler will undergo a modification in May 2007 that will entail the installation of a 

Wet Electrostatic Precipitator to bring the boiler into compliance with the Boiler MACT regulation 

for particulate matter emissions. 

ASHDOWN-Q8-000742